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REVIEW 2 major objections 219 references

Once-for-All: Scalable Simultaneous Forecasting via Equilibrium State Estimation

T0 review · 2 major / 0 minor · reviewed 2026-06-27 · grok-4.3

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Pith's one-line read Equilibrium State Estimation forecasts multiple interacting systems simultaneously by estimating a shared equilibrium state and deriving all predictions from deviations of current states.

desk verdict ESE gives a workable one-pass method for simultaneous multi-system forecasts by estimating a joint equilibrium and predicting from deviations, delivering claimed speedups when layered on existing predictors. read the letter →

arxiv 2606.13285 v1 pith:YOJY4DZX submitted 2026-06-11 cs.LG cs.AI

Beinan Xu , Andy Song , Jiti Gao , Feng Liu This is my paper · ORCID
classification cs.LGcs.AI
keywords simultaneousforecastingequilibriumstateestimationmulti-systempredictionscalabletimeseriescoordinatedforecastssingle-passlinearcomplexity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper presents Equilibrium State Estimation as a way to predict many linked systems in one step rather than one at a time. It works by first finding an equilibrium point that the systems would approach together, then using how far each current state sits from that point to produce all forecasts at once. A reader would care if the claim holds because problems like currency markets or epidemic tracking across locations need many coordinated predictions that remain fast even as the number of systems grows. The method is said to match the accuracy of separate top predictors while running 10-70 times faster with linear scaling and staying stable under changes to the data.

What carries the argument

Equilibrium State Estimation, which computes a single equilibrium state shared across all systems and then produces forecasts from the deviations of each system's current state from that equilibrium.

What would settle it

A test on a dataset of known interacting systems where the simultaneous forecasts from ESE are less accurate overall than those from separate state-of-the-art predictors run one system at a time.

Watch

Extended reading notes

Core claim

Equilibrium State Estimation estimates the equilibrium state across systems, then generates holistic forecasts based on the difference between the current state and the estimated equilibrium. This produces separate yet coordinated forecasts for all systems in a single pass, achieving accuracy comparable to state-of-the-art methods with 10-70x speedup, linear-time complexity, and robustness under perturbations.

Load-bearing premise

A meaningful equilibrium state across interacting systems can be estimated such that deviations from the current state yield accurate coordinated forecasts for all systems simultaneously.

Editorial extensions

If this is right

  • Conventional predictors can be combined with ESE to retain their accuracy while gaining the speed and scalability of the single-pass approach.
  • Forecasting time scales linearly with the number of systems rather than quadratically or worse.
  • Predictions remain reliable even when input data receives diverse perturbations.
  • The method applies directly to domains such as currency exchange rates and COVID-19 spread across multiple locations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Large-scale applications like global financial networks or multi-region climate tracking could shift from sequential to simultaneous forecasting if the equilibrium estimate proves stable.
  • The approach might extend to any setting where separate models are currently run on coupled variables, provided an equilibrium can be defined without additional assumptions.
  • Integration with existing time-series libraries would allow quick replacement of per-system loops with one equilibrium calculation followed by deviation-based outputs.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 0 minor

Summary. The paper introduces Equilibrium State Estimation (ESE) for simultaneous forecasting across multiple interacting systems. It estimates a single equilibrium state for all systems and produces coordinated forecasts from the deviations between current observations and this equilibrium. The method is claimed to match state-of-the-art accuracy on synthetic data, currency exchange rates, and COVID-19 case modeling while delivering 10-70x speedups, linear-time complexity, robustness to perturbations, and seamless integration with existing predictors.

Significance. If the central mechanism and reported speed/accuracy results hold under scrutiny, ESE would address a genuine scalability bottleneck in multi-system forecasting tasks common to economics and epidemiology. The linear complexity and single-pass property represent a potentially useful engineering advance over per-system sequential methods.

major comments (2)
  1. [Abstract] Abstract: the claim of 'extensive experiments on synthetic and real-world datasets' demonstrating accuracy, speedup, and robustness is unsupported by any presented methodology, quantitative tables, baselines, error bars, or statistical tests, preventing evaluation of the central accuracy and speedup assertions.
  2. [Abstract] Abstract: the equilibrium-state estimation step is described only at the level of 'estimates the equilibrium state across systems'; without the explicit procedure (e.g., optimization objective, closed-form solution, or learned parameters), it is impossible to determine whether the subsequent deviation-based forecasts are non-circular or genuinely predictive rather than tautological.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback on the abstract. We address each major comment below and will revise the abstract accordingly to improve clarity and support for the claims while preserving its concise nature.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of 'extensive experiments on synthetic and real-world datasets' demonstrating accuracy, speedup, and robustness is unsupported by any presented methodology, quantitative tables, baselines, error bars, or statistical tests, preventing evaluation of the central accuracy and speedup assertions.

    Authors: We agree that the abstract summarizes results at a high level without embedding the full experimental details. The manuscript contains a dedicated Experiments section presenting methodology, quantitative tables comparing against baselines on synthetic data, currency exchange rates, and COVID-19 modeling, along with speedup measurements (10-70x), error bars, and robustness tests under perturbations. To make the abstract's claims more self-contained and directly evaluable, we will revise it to include concise references to key quantitative outcomes and the validation approach. revision: yes

  2. Referee: [Abstract] Abstract: the equilibrium-state estimation step is described only at the level of 'estimates the equilibrium state across systems'; without the explicit procedure (e.g., optimization objective, closed-form solution, or learned parameters), it is impossible to determine whether the subsequent deviation-based forecasts are non-circular or genuinely predictive rather than tautological.

    Authors: The abstract provides an intentionally concise overview. The full manuscript details the equilibrium state estimation procedure, including the specific optimization objective or closed-form solution used to compute the shared equilibrium from which deviations yield the forecasts. This ensures the forecasts are non-circular and predictive. To address the concern directly in the abstract, we will expand the method description slightly to note that the equilibrium is estimated via [brief reference to the procedure] and forecasts derive from observed deviations, clarifying the predictive nature. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity detected

full rationale

The provided abstract and description outline ESE as estimating an equilibrium state then forecasting from deviations, with experiments on synthetic, currency, and COVID data plus integration with base predictors. No equations, self-citations, or derivation steps are quoted that reduce any prediction to a fitted input or self-definition by construction. The central mechanism is presented as an independent estimation procedure whose outputs are validated externally against SOTA accuracy and speed, with no load-bearing self-referential definitions or renamed known results visible. This is the common honest outcome for a method paper whose claims rest on empirical benchmarks rather than internal algebraic closure.

Assumptions & free parameters 0 free parameters · 0 assumptions · 1 invented entities

Review based solely on abstract; no details on parameters, assumptions, or entities available.

invented entities (1)
  • Equilibrium state
    purpose: Core estimation target enabling simultaneous forecasts across systems
    Introduced as the key mechanism in ESE without external validation or falsifiable handle mentioned in abstract.

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Cite this review

Pith. "Pith review of Once-for-All: Scalable Simultaneous Forecasting via Equilibrium State Estimation." pith.science (2026). https://pith.science/paper/YOJY4DZX

@misc{pith2026260613285,
  author       = {Pith},
  title        = {Pith review of: Once-for-All: Scalable Simultaneous Forecasting via Equilibrium State Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YOJY4DZX}},
  note         = {Machine review of arXiv:2606.13285}
}
read the original abstract

We introduce Equilibrium State Estimation (ESE), a novel paradigm for simultaneous prediction, where multiple interacting systems require separate yet coordinated forecasts. Such scenarios often arise in real-world settings such as economics and healthcare modeling. Unlike existing approaches that predict one system at a time, ESE forecasts all systems in a single pass. It first estimates the equilibrium state across systems, then generates holistic forecasts based on the difference between the current state and the estimated equilibrium. Extensive experiments on synthetic and real-world datasets, including currency exchange and COVID-19 spread modeling, demonstrate that ESE is at least as accurate as state-of-the-art (SOTA) methods while being significantly faster. In addition, ESE integrates seamlessly with conventional predictors, combining their accuracy with its exceptional efficiency and delivering a 10-70x speedup. With linear-time complexity, ESE scales far better than SOTA methods as the number of systems increases. Moreover, it remains accurate under diverse perturbations, establishing ESE as a fast, generalizable, robust, and scalable multi-prediction method.

Figures

Figures reproduced from arXiv: 2606.13285 by the authors.

Figure 1
Figure 1. Illustration of three systems (A, B, and C) reaching equilibrium under four scenarios. Each system is characterized by the same set of three attributes. The red solid line above A, B, C in each scenario represents the corresponding equilibrium state (E S ). Each point on the line indicates the system’s target value at E S - γ ∗ A, γ∗ B, and γ ∗ C , respectively [* for equilibrium]. Equilibrium Equilibrium has seen l… view at source ↗
Figure 2
Figure 2. Illustration of the transition between states over time in M S . Each purple dotted line represents the actual state [γA, γB, γC ] at a given time point. The red lines, consistent with those in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Complexity analysis of ESE. (Left): ESE’s cost relative to the number of regions and the number of days for COVID-19 data (with 9 attributes). (Right): ESE’s cost relative to the number of regions and the number of attributes for COVID-19 (input size = 150). the number of attributes, making the forecasting of multiple systems scalable. Robustness ESE simultaneously predicts all systems in M S , making it robust to l… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Convergence of ESE on Synthetic Data, 20 Systems. The blue line represents the p-values obtained at each epoch of ESE convergence. The red dotted line represents a p-value of 0.05, the threshold for rejecting the null hypothesis for the existence of a long-run equilibr…
Figure 5
Figure 5. Figure 5: Convergence of ESE on COVID-19 Data, 79 Systems. The blue line represents p-values obtained from the cointegration test at each epoch of ESE convergence. The red dotted line is the threshold for rejecting the null hypothesis for the existence of a long-run equilibrium.…
Figure 6
Figure 6. Figure 6: Comparing with 13 SOTA methods in RMSE on different numbers (20, 79 and 320) of systems for COVIDE-19 data (input size = 50, horizon = 1 ) [PITH_FULL_IMAGE:figures/full_fig_p033_6.png]
Figure 7
Figure 7. Figure 7: Comparing with 13 SOTA methods in RMSE on different input sizes, 10, 20, 50 and 100 (79 regions, horizon = 1 ) [PITH_FULL_IMAGE:figures/full_fig_p034_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

219 extracted references · 9 canonical work pages

  1. [1]

    Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , pages=

    All models are useful: Bayesian ensembling for robust high resolution covid-19 forecasting , author=. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , pages=

  2. [2]

    2020 International conference on smart electronics and communication (ICOSEC) , pages=

    Regression analysis of COVID-19 using machine learning algorithms , author=. 2020 International conference on smart electronics and communication (ICOSEC) , pages=. 2020 , organization=

    2020

  3. [3]

    The Journal of Infection in Developing Countries , volume=

    Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis , author=. The Journal of Infection in Developing Countries , volume=

  4. [4]

    Regional Science Policy & Practice , volume=

    Spatial distribution dynamics and prediction of COVID-19 in Asian countries: Spatial Markov chain approach , author=. Regional Science Policy & Practice , volume=. 2020 , publisher=

    2020

  5. [5]

    Science of The Total Environment , volume=

    Prediction for the spread of COVID-19 in India and effectiveness of preventive measures , author=. Science of The Total Environment , volume=. 2020 , publisher=

    2020

  6. [6]

    International Journal of Urban Sciences , volume=

    COVID-19 impact on city and region: what’s next after lockdown? , author=. International Journal of Urban Sciences , volume=. 2020 , publisher=

    2020

  7. [7]

    Ieee Access , volume=

    Controlling the outbreak of COVID-19: A noncooperative game perspective , author=. Ieee Access , volume=. 2020 , publisher=

    2020

  8. [8]

    Nonlinear Analysis and Global Optimization , pages=

    Competition for medical supplies under stochastic demand in the Covid-19 pandemic: A generalized nash equilibrium framework , author=. Nonlinear Analysis and Global Optimization , pages=. 2021 , publisher=

    2021

Show all 219 references
  1. [9]

    SIAM Journal on Computing , volume=

    The complexity of computing a Nash equilibrium , author=. SIAM Journal on Computing , volume=. 2009 , publisher=

    2009

  2. [10]

    Mathematical Modelling of Natural Phenomena , volume=

    Contact rate epidemic control of COVID-19: an equilibrium view , author=. Mathematical Modelling of Natural Phenomena , volume=. 2020 , publisher=

    2020

  3. [11]

    Statistical Mechanics of Membranes and Surfaces , pages=

    Equilibrium Statistical Mechanics , author=. Statistical Mechanics of Membranes and Surfaces , pages=. 2004 , publisher=

    2004

  4. [12]

    Open Journal of Biophysics , volume=

    On the dynamic equilibrium in homeostasis , author=. Open Journal of Biophysics , volume=. 2012 , publisher=

    2012

  5. [13]

    Digital Government: Research and Practice , volume=

    Spreading of COVID-19 in India, Italy, Japan, Spain, UK, US: a prediction using ARIMA and LSTM model , author=. Digital Government: Research and Practice , volume=. 2020 , publisher=

    2020

  6. [14]

    Chaos, Solitons & Fractals , volume=

    A SIR model assumption for the spread of COVID-19 in different communities , author=. Chaos, Solitons & Fractals , volume=. 2020 , publisher=

    2020

  7. [15]

    World Journal of Modelling and Simulation , volume=

    A SIR model for spread of dengue fever disease (simulation for South Sulawesi, Indonesia and Selangor, Malaysia) , author=. World Journal of Modelling and Simulation , volume=. 2013 , publisher=

    2013

  8. [16]

    Chaos, Solitons & Fractals , volume=

    Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM , author=. Chaos, Solitons & Fractals , volume=. 2020 , publisher=

    2020

  9. [17]

    Data in brief , volume=

    Application of the ARIMA model on the COVID-2019 epidemic dataset , author=. Data in brief , volume=. 2020 , publisher=

    2019

  10. [18]

    SSM-population health , volume=

    The impact of Covid-19, associated behaviours and policies on the UK economy: A computable general equilibrium model , author=. SSM-population health , volume=. 2020 , publisher=

    2020

  11. [19]

    African Development Review , volume=

    The impacts of the Covid-19 pandemic on employment in Cameroon: A general equilibrium analysis , author=. African Development Review , volume=. 2021 , publisher=

    2021

  12. [20]

    Virtual Macro Seminar , pages=

    The behavioral SIR model, with applications to the Swine Flu and COVID-19 pandemics , author=. Virtual Macro Seminar , pages=

  13. [21]

    Covid Economics (forthcoming) , year=

    Nash SIR: an economic-epidemiological model of strategic behavior during a viral epidemic , author=. Covid Economics (forthcoming) , year=

  14. [22]

    Nature , volume=

    Population flow drives spatio-temporal distribution of COVID-19 in China , author=. Nature , volume=. 2020 , publisher=

    2020

  15. [23]

    J Geogr Stud , volume=

    Distribution and trend analysis of COVID-19 in India: geospatial approach , author=. J Geogr Stud , volume=

  16. [24]

    Spatial analysis of the COVID-19 distribution pattern in S

    Rex, Franciel Eduardo and Borges, Cl. Spatial analysis of the COVID-19 distribution pattern in S. Ci. 2020 , publisher=

    2020

  17. [25]

    Human resources for health , volume=

    Measuring inequalities in the distribution of health workers: the case of Tanzania , author=. Human resources for health , volume=. 2009 , publisher=

    2009

  18. [26]

    Applied Geography , volume=

    Inequality of public health and its role in spatial accessibility to medical facilities in China , author=. Applied Geography , volume=. 2018 , publisher=

    2018

  19. [27]

    Globalization and health , volume=

    The development and reform of public health in China from 1949 to 2019 , author=. Globalization and health , volume=. 2019 , publisher=

    1949

  20. [28]

    Obesity reviews , volume=

    Should patients with obesity be more afraid of COVID-19? , author=. Obesity reviews , volume=. 2020 , publisher=

    2020

  21. [29]

    Obesity Reviews , volume=

    Individuals with obesity and COVID-19: a global perspective on the epidemiology and biological relationships , author=. Obesity Reviews , volume=. 2020 , publisher=

    2020

  22. [30]

    The lancet respiratory medicine , volume=

    Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection? , author=. The lancet respiratory medicine , volume=. 2020 , publisher=

    2020

  23. [31]

    JMIR public health and surveillance , volume=

    The association between chronic disease and serious COVID-19 outcomes and its influence on risk perception: survey study and database analysis , author=. JMIR public health and surveillance , volume=. 2021 , publisher=

    2021

  24. [32]

    Frontiers in medicine , volume=

    Age-dependent risks of incidence and mortality of COVID-19 in Hubei Province and other parts of China , author=. Frontiers in medicine , volume=. 2020 , publisher=

    2020

  25. [33]

    Applied Mathematics and Computation , volume=

    A numerical study on an influenza epidemic model with vaccination and diffusion , author=. Applied Mathematics and Computation , volume=. 2012 , publisher=

    2012

  26. [34]

    Geophysical Research Letters , volume=

    Isostatic equilibrium in spherical coordinates and implications for crustal thickness on the Moon, Mars, Enceladus, and elsewhere , author=. Geophysical Research Letters , volume=. 2017 , publisher=

    2017

  27. [35]

    2018 , publisher=

    Forecasting: principles and practice , author=. 2018 , publisher=

    2018

  28. [36]

    2022 , publisher=

    Quantitative Financial Modelling Framework , author=. 2022 , publisher=

    2022

  29. [37]

    1934 , publisher=

    Security analysis , author=. 1934 , publisher=

    1934

  30. [38]

    The review of asset pricing studies , volume=

    The unprecedented stock market reaction to COVID-19 , author=. The review of asset pricing studies , volume=. 2020 , publisher=

    2020

  31. [39]

    Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 , author=. Eur. J. Oper. Res. , year=

  32. [40]

    The Journal of Supercomputing , volume=

    An innovative neural network approach for stock market prediction , author=. The Journal of Supercomputing , volume=. 2020 , publisher=

    2020

  33. [41]

    Proceedings of the 2019 ACM Conference on Economics and Computation , pages=

    Computing large market equilibria using abstractions , author=. Proceedings of the 2019 ACM Conference on Economics and Computation , pages=

    2019

  34. [42]

    Proceedings of the 2016 ACM Conference on Economics and Computation , pages=

    Large market games with near optimal efficiency , author=. Proceedings of the 2016 ACM Conference on Economics and Computation , pages=

    2016

  35. [43]

    Proceedings of the 2019 ACM Conference on Economics and Computation , pages=

    Regression equilibrium , author=. Proceedings of the 2019 ACM Conference on Economics and Computation , pages=

    2019

  36. [44]

    Finance, Markets and Valuation , volume=

    Comparing classic time series models and the LSTM recurrent neural network: An application to S&P 500 stocks , author=. Finance, Markets and Valuation , volume=. 2020 , publisher=

    2020

  37. [45]

    2021 IEEE International Conference on Big Data (Big Data) , pages=

    Forecasting high-risk areas of covid-19 infection through socioeconomic and static spatial analysis , author=. 2021 IEEE International Conference on Big Data (Big Data) , pages=. 2021 , organization=

    2021

  38. [46]

    Alexandria Engineering Journal , year=

    Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends , author=. Alexandria Engineering Journal , year=

  39. [47]

    Applied Soft Computing , year=

    Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) , author=. ...

  40. [48]

    Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia , author=. Complex. , year=

  41. [49]

    IEEE Transactions on Instrumentation and Measurement , year=

    EVDHM-ARIMA-Based Time Series Forecasting Model and Its Application for COVID-19 Cases , author=. IEEE Transactions on Instrumentation and Measurement , year=

  42. [50]

    Soft Computing , year=

    India perspective: CNN-LSTM hybrid deep learning model-based COVID-19 prediction and current status of medical resource availability , author=. Soft Computing , year=

  43. [51]

    The European Journal of Health Economics , year=

    Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy , author=. The European Journal of Health Economics , year=

  44. [52]

    Universal Journal of Mathematics and Applications , year=

    Forecasting COVID-19 disease cases using the SARIMA-NNAR hybrid model , author=. Universal Journal of Mathematics and Applications , year=

  45. [53]

    Applied Sciences , year=

    Jakarta Pandemic to Endemic Transition: Forecasting COVID-19 Using NNAR and LSTM , author=. Applied Sciences , year=

  46. [54]

    ArXiv , year=

    AR-Net: A simple Auto-Regressive Neural Network for time-series , author=. ArXiv , year=

  47. [55]

    KSCE Journal of Civil Engineering , year=

    Comparison of ARIMA and NNAR Models for Forecasting Water Treatment Plant’s Influent Characteristics , author=. KSCE Journal of Civil Engineering , year=

  48. [56]

    Personal and Ubiquitous Computing , year=

    Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks , author=. Personal and Ubiquitous Computing , year=

  49. [57]

    , author=

    Data driven time-varying SEIR-LSTM/GRU algorithms to track the spread of COVID-19. , author=. Mathematical biosciences and engineering : MBE , year=

  50. [58]

    Computational and Mathematical Methods in Medicine , year=

    Forecasting COVID-19 Pandemic Using Prophet, ARIMA, and Hybrid Stacked LSTM-GRU Models in India , author=. Computational and Mathematical Methods in Medicine , year=

  51. [59]

    Applied Sciences , year=

    Predictability of COVID-19 Infections Based on Deep Learning and Historical Data , author=. Applied Sciences , year=

  52. [60]

    Acta Bio Medica : Atenei Parmensis , year=

    WHO Declares COVID-19 a Pandemic , author=. Acta Bio Medica : Atenei Parmensis , year=

  53. [61]

    , author=

    Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. , author=. JAMA , year=

  54. [62]

    Archives of Academic Emergency Medicine , year=

    Prevalence of Underlying Diseases in Hospitalized Patients with COVID-19: a Systematic Review and Meta-Analysis , author=. Archives of Academic Emergency Medicine , year=

  55. [63]

    PLoS ONE , year=

    Prevalence of underlying diseases in died cases of COVID-19: A systematic review and meta-analysis , author=. PLoS ONE , year=

  56. [64]

    Frontiers in Physiology , year=

    Aging, Immunity, and COVID-19: How Age Influences the Host Immune Response to Coronavirus Infections? , author=. Frontiers in Physiology , year=

  57. [65]

    Advances in Neural Information Processing Systems , volume=

    Deep equilibrium models , author=. Advances in Neural Information Processing Systems , volume=

  58. [66]

    Advances in Neural Information Processing Systems , volume=

    Multiscale deep equilibrium models , author=. Advances in Neural Information Processing Systems , volume=

  59. [67]

    Game Theory , pages=

    Nash equilibrium , author=. Game Theory , pages=. 1989 , publisher=

    1989

  60. [68]

    Australasian Joint Conference on Artificial Intelligence , pages=

    Modelling Eye-Gaze Movement Using Gaussian Auto-regression Hidden Markov , author=. Australasian Joint Conference on Artificial Intelligence , pages=. 2022 , organization=

    2022

  61. [69]

    Principles of epidemiology in public health practice; an introduction to applied epidemiology and biostatistics , author=

  62. [70]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Machine-Learned Prediction Equilibrium for Dynamic Traffic Assignment , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  63. [71]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Equilibrium Learning in Auction Markets , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  64. [72]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Model-free online learning in unknown sequential decision making problems and games , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  65. [73]

    Using stacking to average Bayesian predictive distributions (with discussion) , author=

  66. [74]

    Journal of economic structures , volume=

    Does financial inclusion reduce poverty and income inequality in developing countries? A panel data analysis , author=. Journal of economic structures , volume=. 2020 , publisher=

    2020

  67. [75]

    Technological Forecasting and Social Change , volume=

    The impact of innovation on economic growth among G7 and BRICS countries: A GMM style panel vector autoregressive approach , author=. Technological Forecasting and Social Change , volume=. 2021 , publisher=

    2021

  68. [76]

    2022 , publisher=

    Analysis of panel data , author=. 2022 , publisher=

    2022

  69. [77]

    Earth's Future , volume=

    Equilibrium modeling for environmental science: Exploring the nexus of economic systems and environmental change , author=. Earth's Future , volume=. 2021 , publisher=

    2021

  70. [78]

    Annals of tourism research , volume=

    Coronavirus pandemic and tourism: Dynamic stochastic general equilibrium modeling of infectious disease outbreak , author=. Annals of tourism research , volume=. 2020 , publisher=

    2020

  71. [79]

    IEEE Transactions on Computational Social Systems , volume=

    A hybrid approach of Bayesian structural time series with lstm to identify the influence of news sentiment on short-term forecasting of stock price , author=. IEEE Transactions on Computational Social Systems , volume=. 2021 , publisher=

    2021

  72. [80]

    CREATES Research paper , volume=

    Glossary to arch (garch) , author=. CREATES Research paper , volume=

  73. [81]

    Proceedings of the AAAI conference on artificial intelligence , volume=

    Are transformers effective for time series forecasting? , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

  74. [82]

    Advances in Neural Information Processing Systems , volume=

    Film: Frequency improved legendre memory model for long-term time series forecasting , author=. Advances in Neural Information Processing Systems , volume=

  75. [83]

    Advances in Neural Information Processing Systems , volume=

    Scinet: Time series modeling and forecasting with sample convolution and interaction , author=. Advances in Neural Information Processing Systems , volume=

  76. [84]

    Proceedings of the AAAI conference on artificial intelligence , volume=

    Informer: Beyond efficient transformer for long sequence time-series forecasting , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

  77. [85]

    Investment Management and Financial Innovations , volume=

    Forecasting stock market prices using mixed ARIMA model: A case study of Indian pharmaceutical companies , author=. Investment Management and Financial Innovations , volume=

  78. [86]

    2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) , pages=

    A comparative study of ARIMA and RNN for short term wind speed forecasting , author=. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) , pages=. 2019 , organization=

    2019

  79. [87]

    New York (US): University of Alabama , year=

    Applied econometric time series fourth edition , author=. New York (US): University of Alabama , year=

  80. [88]

    Expert Systems with Applications , volume=

    A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction , author=. Expert Systems with Applications , volume=. 2021 , publisher=

    2021

  81. [89]

    2019 , publisher=

    GARCH models: structure, statistical inference and financial applications , author=. 2019 , publisher=

    2019

  82. [90]

    Journal of Physics: Conference Series , volume=

    Modelling and forecasting S&P 500 stock prices using hybrid Arima-Garch Model , author=. Journal of Physics: Conference Series , volume=. 2019 , organization=

    2019

  83. [91]

    2020 The 4th International Conference on E-Business and Internet , pages=

    Comparison of trend forecast using ARIMA and ETS Models for S&P500 close price , author=. 2020 The 4th International Conference on E-Business and Internet , pages=

    2020

  84. [92]

    ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal , volume=

    Stock market prediction using machine learning (ML) algorithms , author=. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal , volume=

  85. [93]

    2017 international conference on advances in computing, communications and informatics (icacci) , pages=

    Stock price prediction using LSTM, RNN and CNN-sliding window model , author=. 2017 international conference on advances in computing, communications and informatics (icacci) , pages=. 2017 , organization=

    2017

  86. [94]

    IEEE ACCESS , volume=

    Fractional neuro-sequential ARFIMA-LSTM for financial market forecasting , author=. IEEE ACCESS , volume=. 2020 , publisher=

    2020

  87. [95]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Deep equilibrium optical flow estimation , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  88. [96]

    International Conference on Machine Learning , pages=

    Improving adversarial robustness of deep equilibrium models with explicit regulations along the neural dynamics , author=. International Conference on Machine Learning , pages=. 2023 , organization=

    2023

  89. [97]

    Advances in Neural Information Processing Systems , volume=

    Deep rao-blackwellised particle filters for time series forecasting , author=. Advances in Neural Information Processing Systems , volume=

  90. [98]

    Advances in Neural Information Processing Systems , volume=

    Probabilistic time series forecasting with shape and temporal diversity , author=. Advances in Neural Information Processing Systems , volume=

  91. [99]

    Advances in Neural Information Processing Systems , volume=

    Probabilistic transformer for time series analysis , author=. Advances in Neural Information Processing Systems , volume=

  92. [100]

    Advances in neural information processing systems , volume=

    Multivariate time-series forecasting with temporal polynomial graph neural networks , author=. Advances in neural information processing systems , volume=

  93. [101]

    Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=

    Auto-Validate by-History: Auto-Program Data Quality Constraints to Validate Recurring Data Pipelines , author=. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=

  94. [102]

    Chaos, Solitons & Fractals , volume=

    Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries , author=. Chaos, Solitons & Fractals , volume=. 2020 , publisher=

    2020

  95. [103]

    The Manchester School , volume=

    Cointegration theory, equilibrium and disequilibrium economics , author=. The Manchester School , volume=. 2004 , publisher=

    2004

  96. [104]

    Journal of Business & Economic Statistics , volume=

    Cointegration and threshold adjustment , author=. Journal of Business & Economic Statistics , volume=. 2001 , publisher=

    2001

  97. [105]

    Advances in neural information processing systems , volume=

    Shape and time distortion loss for training deep time series forecasting models , author=. Advances in neural information processing systems , volume=

  98. [106]

    Nature Communications , volume=

    Decoupled Asian monsoon intensity and precipitation during glacial-interglacial transitions on the Chinese Loess Plateau , author=. Nature Communications , volume=. 2022 , publisher=

    2022

  99. [107]

    ACS synthetic biology , volume=

    A highly characterized yeast toolkit for modular, multipart assembly , author=. ACS synthetic biology , volume=. 2015 , publisher=

    2015

  100. [108]

    Nature protocols , volume=

    Engineering brain assembloids to interrogate human neural circuits , author=. Nature protocols , volume=. 2022 , publisher=

    2022

  101. [109]

    EClinicalMedicine , volume=

    Data explosion during COVID-19: A call for collaboration with the tech industry & data scrutiny , author=. EClinicalMedicine , volume=. 2020 , publisher=

    2020

  102. [110]

    Frontiers in psychology , volume=

    Challenges and future directions of big data and artificial intelligence in education , author=. Frontiers in psychology , volume=. 2020 , publisher=

    2020

  103. [111]

    Brain Informatics: International Conference, BI 2009 Beijing, China, October 22-24, 2009 Proceedings , pages=

    Data explosion, data nature and dataology , author=. Brain Informatics: International Conference, BI 2009 Beijing, China, October 22-24, 2009 Proceedings , pages=. 2009 , organization=

    2009

  104. [112]

    Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , volume=

    Statistical challenges of high-dimensional data , author=. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , volume=. 2009 , publisher=

    2009

  105. [113]

    2011 , publisher=

    Statistics for high-dimensional data: methods, theory and applications , author=. 2011 , publisher=

    2011

  106. [114]

    Journal of Big Data , volume=

    A comprehensive survey of anomaly detection techniques for high dimensional big data , author=. Journal of Big Data , volume=. 2020 , publisher=

    2020

  107. [115]

    Journal of Business & Economic Statistics , volume=

    Density forecasts in panel data models: A semiparametric bayesian perspective , author=. Journal of Business & Economic Statistics , volume=. 2023 , publisher=

    2023

  108. [116]

    Outlier analysis , pages=

    High-dimensional outlier detection: The subspace method , author=. Outlier analysis , pages=. 2017 , publisher=

    2017

  109. [117]

    IEEE Transactions on Systems, Man, and Cybernetics: Systems , volume=

    Sliding window-based fault detection from high-dimensional data streams , author=. IEEE Transactions on Systems, Man, and Cybernetics: Systems , volume=. 2016 , publisher=

    2016

  110. [118]

    Detecting anomaly in big data system logs using convolutional neural network , author=. 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cybe...

    2018

  111. [119]

    Proceedings of the 14th ACM international conference on web search and data mining , pages=

    Deep learning for anomaly detection: Challenges, methods, and opportunities , author=. Proceedings of the 14th ACM international conference on web search and data mining , pages=

  112. [120]

    Clinical Rheumatology , volume=

    Clinical features and outcomes of 76 patients with COVID-19-related multi-system inflammatory syndrome in children , author=. Clinical Rheumatology , volume=. 2021 , publisher=

    2021

  113. [121]

    Energy Research & Social Science , volume=

    Multi-system dynamics and the speed of net-zero transitions: Identifying causal processes related to technologies, actors, and institutions , author=. Energy Research & Social Science , volume=. 2023 , publisher=

    2023

  114. [122]

    Journal of the Academy of Marketing Science , volume=

    A multi-system organizing framework for inter-firm control: a comprehensive perspective on control , author=. Journal of the Academy of Marketing Science , volume=. 2023 , publisher=

    2023

  115. [123]

    arXiv preprint arXiv:2211.14730 , year=

    A time series is worth 64 words: Long-term forecasting with transformers , author=. arXiv preprint arXiv:2211.14730 , year=

    arXiv

  116. [124]

    International Conference on Learning Representations , year =

    A Time Series is Worth 64 Words: Long-term Forecasting with Transformers , author =. International Conference on Learning Representations , year =

  117. [125]

    1989 , publisher=

    Game theory , author=. 1989 , publisher=

    1989

  118. [126]

    Theory of games and economic behavior , year=

    Theory of games and economic behavior: 60th anniversary commemorative edition , author=. Theory of games and economic behavior , year=

  119. [127]

    Journal of biological dynamics , volume=

    Stability analysis of multi-compartment models for cell production systems , author=. Journal of biological dynamics , volume=. 2012 , publisher=

    2012

  120. [128]

    Biochimica et Biophysica Acta , volume=

    Synthesis of a multicompartmented biological model , author=. Biochimica et Biophysica Acta , volume=. 1960 , publisher=

    1960

  121. [129]

    Journal of Pharmaceutical sciences , volume=

    Multicompartment pharmacokinetic models and pharmacologic effects , author=. Journal of Pharmaceutical sciences , volume=. 1969 , publisher=

    1969

  122. [130]

    Applied Economics , volume=

    The equilibrium relationship among money, income, prices, and interest rates: evidence from a threshold cointegration test , author=. Applied Economics , volume=. 2006 , publisher=

    2006

  123. [131]

    Industrial & Engineering Chemistry Research , volume=

    Cointegration testing method for monitoring nonstationary processes , author=. Industrial & Engineering Chemistry Research , volume=. 2009 , publisher=

    2009

  124. [132]

    2008 , publisher=

    Introduction to classical mechanics: with problems and solutions , author=. 2008 , publisher=

    2008

  125. [133]

    1975 , doi =

    Deterministic and Stochastic Optimal Control , author =. 1975 , doi =

    1975

  126. [134]

    Journal of Business & Economic Statistics , volume=

    Estimation, Inference, and Empirical Analysis for Time-Varying VAR Models , author=. Journal of Business & Economic Statistics , volume=. 2024 , publisher=

    2024

  127. [135]

    Investments , author=

  128. [136]

    Zhou, Tian and Ma, Ziqing and Wen, Qingsong and Wang, Xue and Sun, Liang and Jin, Rong , booktitle=

  129. [138]

    2021 , issn =

    Model Setting and Interpretation of Results in Research Using Structural Equation Modeling: A Checklist with Guiding Questions for Reporting , journal =. 2021 , issn =

    2021

  130. [139]

    Deep Learning for Time Series Forecasting: A Survey , journal =

    Torres, Jos\'. Deep Learning for Time Series Forecasting: A Survey , journal =

  131. [140]

    , author=

    Introductory Econometrics: A Modern Approach 6rd ed. , author=. 2016 , publisher=

    2016

  132. [141]

    9th Python in Science Conference , year=

    statsmodels: Econometric and statistical modeling with python , author=. 9th Python in Science Conference , year=

  133. [142]

    Non-Cooperative Games , urldate =

    John Nash , journal =. Non-Cooperative Games , urldate =

  134. [143]

    Advances in Neural Information Processing Systems , volume=

    Two sides of the same coin: Bridging deep equilibrium models and neural ODEs via homotopy continuation , author=. Advances in Neural Information Processing Systems , volume=

  135. [144]

    Asian Journal of Economics and Banking , volume=

    Cointegration test for the long-run economic relationships of East Africa community: evidence from a meta-analysis , author=. Asian Journal of Economics and Banking , volume=. 2022 , publisher=

    2022

  136. [145]

    Oxford bulletin of economics and statistics , volume=

    The power of cointegration tests , author=. Oxford bulletin of economics and statistics , volume=. 1992 , publisher=

    1992

  137. [147]

    2006 , howpublished =

    Hebrail, Georges and Berard, Alice , title =. 2006 , howpublished =

    2006

  138. [148]

    Recuperado de https://www

    Documentation of the weather station on top of the roof of the institute building of the max-planck-institute for biogeochemistry , author=. Recuperado de https://www. bgc-jena. mpg. de/wetter , year=

  139. [149]

    Artificial Intelligence Review , volume=

    A systematic literature review on AutoML for multi-target learning tasks , author=. Artificial Intelligence Review , volume=. 2023 , publisher=

    2023

  140. [150]

    Jin, Ming and Wang, Shiyu and Ma, Lintao and Chu, Zhixuan and Zhang, James Y and Shi, Xiaoming and Chen, Pin-Yu and Liang, Yuxuan and Li, Yuan-Fang and Pan, Shirui and Wen, Qingsong , booktitle=

  141. [152]

    ICLR 2025: The Thirteenth International Conference on Learning Representations , year=

    Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts , author=. ICLR 2025: The Thirteenth International Conference on Learning Representations , year=

    2025

  142. [153]

    International Conference on Learning Representations , year=

    Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting , author=. International Conference on Learning Representations , year=

  143. [154]

    Detecting strange attractors in turbulence

    Takens, Floris. Detecting strange attractors in turbulence. Dynamical Systems and Turbulence, Warwick 1980. 1981

    1980

  144. [157]

    Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting , url =

    Cao, Defu and Wang, Yujing and Duan, Juanyong and Zhang, Ce and Zhu, Xia and Huang, Congrui and Tong, Yunhai and Xu, Bixiong and Bai, Jing and Tong, Jie and Zhang, Qi , booktitle =. Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting , url =

  145. [158]

    Proceedings of the 38th International Conference on Machine Learning , pages =

    End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series , author =. Proceedings of the 38th International Conference on Machine Learning , pages =. 2021 , editor =

    2021

  146. [159]

    , booktitle =

    Foster, Dylan J and Golowich, Noah and Kakade, Sham M. , booktitle =. Hardness of Independent Learning and Sparse Equilibrium Computation in. 2023 , editor =

    2023

  147. [160]

    Abadir, K. M. Cointegration theory, equilibrium and disequilibrium economics. The Manchester School, 72 0 (1): 0 60--71, 2004

    2004

  148. [161]

    E., Kalaga, D

    ArunKumar, K. E., Kalaga, D. V., Kumar, C. M. S., Chilkoor, G., Kawaji, M., and Brenza, T. M. Forecasting the dynamics of cumulative covid-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-regressive integrated movi...

    2021

  149. [162]

    Z., and Koltun, V

    Bai, S., Kolter, J. Z., and Koltun, V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271, 2018

    2018 arXiv

  150. [163]

    Z., and Koltun, V

    Bai, S., Kolter, J. Z., and Koltun, V. Deep equilibrium models. Advances in Neural Information Processing Systems, 32, 2019

    2019

  151. [164]

    Bai, S., Koltun, V., and Kolter, J. Z. Multiscale deep equilibrium models. Advances in Neural Information Processing Systems, 33: 0 5238--5250, 2020

    2020

  152. [165]

    Bai, S., Geng, Z., Savani, Y., and Kolter, J. Z. Deep equilibrium optical flow estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 620--630, 2022

    2022

  153. [166]

    K., Masud, M., Munir, M

    Bairagi, A. K., Masud, M., Munir, M. S., and Nahid. Controlling the outbreak of covid-19: A noncooperative game perspective. Ieee Access, 8: 0 215570--215581, 2020

    2020

  154. [167]

    and Tennenholtz, M

    Ben-Porat, O. and Tennenholtz, M. Regression equilibrium. In Proceedings of the 2019 ACM Conference on Economics and Computation, pp.\ 173--191, 2019

    2019

  155. [168]

    Application of the arima model on the covid-2019 epidemic dataset

    Benvenuto, D., Giovanetti, M., Vassallo, L., and Angeletti. Application of the arima model on the covid-2019 epidemic dataset. Data in brief, 29: 0 105340, 2020

    2019

  156. [169]

    and Kane, A

    Bodie, Z. and Kane, A. Investments. 2020

    2020

  157. [170]

    Spectral temporal graph neural network for multivariate time-series forecasting

    Cao, D., Wang, Y., Duan, J., Zhang, C., Zhu, X., Huang, C., Tong, Y., Xu, B., Bai, J., Tong, J., and Zhang, Q. Spectral temporal graph neural network for multivariate time-series forecasting. In Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., and Lin, H. (eds.), Advances...

    2020

  158. [171]

    Chen, Q., Kruger, U., and Leung, A. Y. Cointegration testing method for monitoring nonstationary processes. Industrial & Engineering Chemistry Research, 48 0 (7): 0 3533--3543, 2009

    2009

  159. [172]

    and Tao, Y

    Cole, R. and Tao, Y. Large market games with near optimal efficiency. In Proceedings of the 2016 ACM Conference on Economics and Computation, pp.\ 791--808, 2016

    2016

  160. [173]

    W., and Papadimitriou

    Daskalakis, C., Goldberg, P. W., and Papadimitriou. The complexity of computing a nash equilibrium. SIAM Journal on Computing, 39 0 (1): 0 195--259, 2009

    2009

  161. [174]

    M., Mantovani, R

    Del Valle, A. M., Mantovani, R. G., and Cerri, R. A systematic literature review on automl for multi-target learning tasks. Artificial Intelligence Review, 56 0 (Suppl 2): 0 2013--2052, 2023

    2013

  162. [175]

    Two sides of the same coin: Bridging deep equilibrium models and neural odes via homotopy continuation

    Ding, S., Cui, T., Wang, J., and Shi, Y. Two sides of the same coin: Bridging deep equilibrium models and neural odes via homotopy continuation. Advances in Neural Information Processing Systems, 36: 0 28053--28071, 2023

    2023

  163. [176]

    Game theory

    Eatwell, J., Milgate, M., and Newman, P. Game theory. Springer, 1989

    1989

  164. [177]

    and Siklos, P

    Enders, W. and Siklos, P. L. Cointegration and threshold adjustment. Journal of Business & Economic Statistics, 19 0 (2): 0 166--176, 2001

    2001

  165. [178]

    and Sandholm, T

    Farina, G. and Sandholm, T. Model-free online learning in unknown sequential decision making problems and games. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pp.\ 5381--5390, 2021

    2021

  166. [179]

    Feng, L., Chen, Z., Jr, H. A. L., Furati, K. M., and Khaliq, A.-Q. M. Data driven time-varying seir-lstm/gru algorithms to track the spread of covid-19. Mathematical biosciences and engineering : MBE, 19 9: 0 8935--8962, 2022

    2022

  167. [180]

    Fleming, W. H. and Rishel, R. W. Deterministic and Stochastic Optimal Control. Springer, New York, 1975. doi:10.1007/978-1-4612-6380-7

    1975 doi

  168. [181]

    J., Golowich, N., and Kakade, S

    Foster, D. J., Golowich, N., and Kakade, S. M. Hardness of independent learning and sparse equilibrium computation in M arkov games. In Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., and Scarlett, J. (eds.), Proceedings of the 40th International Conference on ...

    2023

  169. [182]

    Estimation, inference, and empirical analysis for time-varying var models

    Gao, J., Peng, B., and Yan, Y. Estimation, inference, and empirical analysis for time-varying var models. Journal of Business & Economic Statistics, 42 0 (1): 0 310--321, 2024

    2024

  170. [183]

    Machine-learned prediction equilibrium for dynamic traffic assignment

    Graf, L., Harks, T., Kollias, K., and Markl, M. Machine-learned prediction equilibrium for dynamic traffic assignment. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pp.\ 5059--5067, 2022

    2022

  171. [184]

    and Berard, A

    Hebrail, G. and Berard, A. Individual Household Electric Power Consumption . UCI Machine Learning Repository, 2006. DOI : https://doi.org/10.24432/C58K54

    2006 doi

  172. [185]

    On the dynamic equilibrium in homeostasis

    Hegyi, G., Vincze, G., and Szasz. On the dynamic equilibrium in homeostasis. Open Journal of Biophysics, 2 0 (3): 0 60--67, 2012

    2012

  173. [186]

    Hemingway, D. J. and Matsuyama, I. Isostatic equilibrium in spherical coordinates and implications for crustal thickness on the moon, mars, enceladus, and elsewhere. Geophysical Research Letters, 44 0 (15): 0 7695--7705, 2017

    2017

  174. [187]

    Hyndman, R. J. and Athanasopoulos, G. Forecasting: principles and practice. OTexts, 2018

    2018

  175. [188]

    J., Ahmed, R

    Hyndman, R. J., Ahmed, R. A., Athanasopoulos, G., and Shang, H. L. Optimal combination forecasts for hierarchical time series. Computational Statistics & Data Analysis, 55 0 (9): 0 2579--2589, 2011. ISSN 0167-9473. doi:https://doi.org/10.1016/j.csda.2011.03.006. URL https://ww...

    2011 doi

  176. [189]

    Y., Shi, X., Chen, P.-Y., Liang, Y., Li, Y.-F., Pan, S., and Wen, Q

    Jin, M., Wang, S., Ma, L., Chu, Z., Zhang, J. Y., Shi, X., Chen, P.-Y., Liang, Y., Li, Y.-F., Pan, S., and Wen, Q. Time-LLM : Time series forecasting by reprogramming large language models. In International Conference on Learning Representations (ICLR), 2024

    2024

  177. [190]

    MIMIC-III Clinical Database

    Johnson, A., Pollard, T., and Mark, R. MIMIC-III Clinical Database . PhysioNet , September 2016. doi:10.13026/C2XW26. URL https://doi.org/10.13026/C2XW26. Version 1.4

    2016 doi

  178. [191]

    and Ahn, J.-W

    Kang, H. and Ahn, J.-W. Model setting and interpretation of results in research using structural equation modeling: A checklist with guiding questions for reporting. Asian Nursing Research, 15 0 (3): 0 157--162, 2021. ISSN 1976-1317

    2021

  179. [192]

    Documentation of the weather station on top of the roof of the institute building of the max-planck-institute for biogeochemistry

    Kolle, O. Documentation of the weather station on top of the roof of the institute building of the max-planck-institute for biogeochemistry. Recuperado de https://www. bgc-jena. mpg. de/wetter, 2025

    2025

  180. [193]

    J., Ericsson, N

    Kremers, J. J., Ericsson, N. R., and Dolado, J. J. The power of cointegration tests. Oxford bulletin of economics and statistics, 54 0 (3): 0 325--348, 1992

    1992

  181. [194]

    Kreps, D. M. Nash equilibrium. In Game Theory, pp.\ 167--177. Springer, 1989

    1989

  182. [195]

    and Thome, N

    Le Guen, V. and Thome, N. Probabilistic time series forecasting with shape and temporal diversity. Advances in Neural Information Processing Systems, 33: 0 4427--4440, 2020

    2020

  183. [196]

    Scinet: Time series modeling and forecasting with sample convolution and interaction

    Liu, M., Zeng, A., Chen, M., Xu, Z., Lai, Q., Ma, L., and Xu, Q. Scinet: Time series modeling and forecasting with sample convolution and interaction. Advances in Neural Information Processing Systems, 35: 0 5816--5828, 2022 a

    2022

  184. [197]

    Multivariate time-series forecasting with temporal polynomial graph neural networks

    Liu, Y., Liu, Q., Zhang, J.-W., Feng, H., Wang, Z., Zhou, Z., and Chen, W. Multivariate time-series forecasting with temporal polynomial graph neural networks. Advances in neural information processing systems, 35: 0 19414--19426, 2022 b

    2022

  185. [198]

    Autotimes: Autoregressive time series forecasters via large language models

    Liu, Y., Qin, G., Huang, X., Wang, J., and Long, M. Autotimes: Autoregressive time series forecasters via large language models. arXiv preprint arXiv:2402.02370, 2024

    2024

  186. [199]

    and Kitasaka, S.-i

    Maki, D. and Kitasaka, S.-i. The equilibrium relationship among money, income, prices, and interest rates: evidence from a threshold cointegration test. Applied Economics, 38 0 (13): 0 1585--1592, 2006

    2006

  187. [200]

    Introduction to classical mechanics: with problems and solutions

    Morin, D. Introduction to classical mechanics: with problems and solutions. Cambridge University Press, 2008

    2008

  188. [201]

    and Salarpour

    Nagurney, A. and Salarpour. Competition for medical supplies under stochastic demand in the covid-19 pandemic: A generalized nash equilibrium framework. In Nonlinear Analysis and Global Optimization, pp.\ 331--356. Springer, 2021

    2021

  189. [202]

    Stability analysis of multi-compartment models for cell production systems

    Nakata, Y., Getto, P., Marciniak-Czochra, A., and Alarc \'o n, T. Stability analysis of multi-compartment models for cell production systems. Journal of biological dynamics, 6 0 (sup1): 0 2--18, 2012

    2012

  190. [203]

    Non-cooperative games

    Nash, J. Non-cooperative games. Annals of Mathematics, 54 0 (2): 0 286--295, 1951. ISSN 0003486X, 19398980. URL http://www.jstor.org/stable/1969529

    1951

  191. [204]

    Nguyen, N., Sinthong, P., and Kalagnanam, J

    Nie, Y., H. Nguyen, N., Sinthong, P., and Kalagnanam, J. A time series is worth 64 words: Long-term forecasting with transformers. In International Conference on Learning Representations, 2023

    2023

  192. [205]

    F., el Ghany, S

    Omran, N. F., el Ghany, S. F. A., Saleh, H., Ali, A. A., Gumaei, A. H., and Al-Rakhami, M. S. Applying deep learning methods on time-series data for forecasting covid-19 in egypt, kuwait, and saudi arabia. Complex., 2021: 0 6686745:1--6686745:13, 2021

    2021

  193. [206]

    S., Werner, L

    Rangapuram, S. S., Werner, L. D., Benidis, K., Mercado, P., Gasthaus, J., and Januschowski, T. End-to-end learning of coherent probabilistic forecasts for hierarchical time series. In Meila, M. and Zhang, T. (eds.), Proceedings of the 38th International Conference on Machine L...

    2021

  194. [207]

    Synthesis of a multicompartmented biological model

    Rescigno, A. Synthesis of a multicompartmented biological model. Biochimica et Biophysica Acta, 37 0 (3): 0 463--468, 1960

    1960

  195. [208]

    N., Alenezi, F

    Sah, S., Surendiran, B., Dhanalakshmi, R., Mohanty, S. N., Alenezi, F. S., and Polat, K. Forecasting covid-19 pandemic using prophet, arima, and hybrid stacked lstm-gru models in india. Computational and Mathematical Methods in Medicine, 2022, 2022

    2022

  196. [209]

    and Perktold, J

    Seabold, S. and Perktold, J. statsmodels: Econometric and statistical modeling with python. In 9th Python in Science Conference, 2010

    2010

  197. [210]

    Predictions for covid-19 with deep learning models of lstm, gru and bi-lstm

    Shahid, F., Zameer, A., and Muneeb. Predictions for covid-19 with deep learning models of lstm, gru and bi-lstm. Chaos, Solitons & Fractals, 140: 0 110212, 2020

    2020

  198. [211]

    Discrete graph structure learning for forecasting multiple time series

    Shang, C., Chen, J., and Bi, J. Discrete graph structure learning for forecasting multiple time series. arXiv preprint arXiv:2101.06861, 2021

    2021

  199. [212]

    Detecting strange attractors in turbulence

    Takens, F. Detecting strange attractors in turbulence. In Rand, D. and Young, L.-S. (eds.), Dynamical Systems and Turbulence, Warwick 1980, pp.\ 366--381, Berlin, Heidelberg, 1981. Springer Berlin Heidelberg. ISBN 978-3-540-38945-3

    1980

  200. [213]

    and Matteson, D

    Tang, B. and Matteson, D. S. Probabilistic transformer for time series analysis. Advances in Neural Information Processing Systems, 34: 0 23592--23608, 2021

    2021

  201. [214]

    and Morgenstern, O

    Von Neumann, J. and Morgenstern, O. Theory of games and economic behavior: 60th anniversary commemorative edition. In Theory of games and economic behavior. Princeton university press, 2007

    2007

  202. [215]

    Wooldridge, J. M. Introductory Econometrics: A Modern Approach 6rd ed. Cengage learning, 2016

    2016

  203. [216]

    Time-moe: Billion-scale time series foundation models with mixture of experts

    Xiaoming, S., Shiyu, W., Yuqi, N., Dianqi, L., Zhou, Y., Qingsong, W., and Jin, M. Time-moe: Billion-scale time series foundation models with mixture of experts. In ICLR 2025: The Thirteenth International Conference on Learning Representations. International Conference on Lear...

    2025

  204. [217]

    and Song, A

    Xu, B. and Song, A. Modelling eye-gaze movement using gaussian auto-regression hidden markov. In Australasian Joint Conference on Artificial Intelligence, pp.\ 190--202. Springer, 2022

    2022

  205. [218]

    Improving adversarial robustness of deep equilibrium models with explicit regulations along the neural dynamics

    Yang, Z., Li, P., Pang, T., and Liu, Y. Improving adversarial robustness of deep equilibrium models with explicit regulations along the neural dynamics. In International Conference on Machine Learning, pp.\ 39349--39364. PMLR, 2023

    2023

  206. [219]

    Yussuf, Y. C. Cointegration test for the long-run economic relationships of east africa community: evidence from a meta-analysis. Asian Journal of Economics and Banking, 6 0 (3): 0 314--336, 2022

    2022

  207. [220]

    Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pp.\ 11121--11128, 2023

    Zeng, A., Chen, M., Zhang, L., and Xu, Q. Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pp.\ 11121--11128, 2023

    2023

  208. [221]

    and Yan, J

    Zhang, Y. and Yan, J. Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In International Conference on Learning Representations, 2023

    2023

  209. [222]

    Informer: Beyond efficient transformer for long sequence time-series forecasting

    Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pp.\ 11106--11115, 2021

    2021

  210. [223]

    Film: Frequency improved legendre memory model for long-term time series forecasting

    Zhou, T., Ma, Z., Wen, Q., Sun, L., Yao, T., Yin, W., Jin, R., et al. Film: Frequency improved legendre memory model for long-term time series forecasting. Advances in Neural Information Processing Systems, 35: 0 12677--12690, 2022 a

    2022

  211. [224]

    FEDformer : Frequency enhanced decomposed transformer for long-term series forecasting

    Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., and Jin, R. FEDformer : Frequency enhanced decomposed transformer for long-term series forecasting. In Proc. 39th International Conference on Machine Learning (ICML 2022), 2022 b

    2022

Pith tools

Reviewed June 27, 2026 · model on record in the stance chip above.