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REVIEW 3 major objections 5 minor 103 references

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read The paper's central claim is that an ordinary, semi-automatic RNN setup—stacked LSTM with peephole connections, COCOB optimizer, deseasonalized moving-window inputs—is competitive with ETS and ARIMA across many datasets and beats them on…

desk verdict A broad and reproducible empirical study that supports RNNs as competitive forecasting tools, but the specific 'best' configuration advice should be read as heuristic, not proof. read the letter →

arxiv 1909.00590 v5 pith:66ETS4PT submitted 2019-09-02 cs.LG cs.NEstat.ML

Hansika Hewamalage , Christoph Bergmeir , Kasun Bandara This is my paper · ORCID
classification cs.LGcs.NEstat.ML
keywords timeseriesforecastingrecurrentneuralnetworksstackedLSTMarchitecturepeepholeconnectionsCOCOBoptimizerdeseasonalizationglobalmodelsETSandARIMAbenchmarks
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

This paper asks whether recurrent neural networks can be made practical for everyday forecasting, not just for competition-winning expert systems. Its answer is yes under a specific, semi-automatic recipe: use a stacked architecture of LSTM cells with peephole connections, train one global model across all available series with the COCOB optimizer (which removes the learning-rate hyperparameter), feed the data through moving windows, and remove deterministic seasonality before training unless the series all share one aligned seasonal pattern. Across six public univariate competition datasets, that combination is competitive with automatic ETS and ARIMA, and beats them on several—though not on the M4 monthly set as a whole. The paper matters because it turns RNN forecasting into a reproducible, semi-automatic procedure with explicit guidelines rather than a black box that only experts can tune. Its own conclusion is measured: RNNs are now a good option for practitioners, not a universal replacement for classical methods.

What carries the argument

The load-bearing mechanism is the paper's 'Stacked architecture': several recurrent layers in which the same cell repeats through time, each layer passing its output to the next, and a final dense layer projecting the last hidden state to the full forecast horizon. Around that core, four components carry most of the argument: LSTM cells with peephole connections, which let the gates read the cell state directly; the COCOB optimizer, which removes the learning rate from the set of hyperparameters to tune; STL decomposition for deseasonalization; and training one global model on all available series so that information is shared across them. A moving-window input scheme turns each series into many overlapping input–output pairs, which the paper uses as a form of data augmentation and which makes the multi-step-ahead forecast a single multiple-output prediction rather than a recursive chain. The experiments are designed so that each of these choices can be turned on or off, which is how the paper derives its guidelines.

What would settle it

Re-run the six datasets and models with a much larger tuning budget—say 300 evaluations per configuration instead of 50—and see whether the stacked LSTM with peephole connections, COCOB, deseasonalization, and moving windows is still the best on most datasets. If another architecture overtakes it, the central recipe is an artifact of the tuning budget.

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Extended reading notes

Core claim

The study's central claim, stated in the authors' own conclusion, is that 'the Stacked architecture combined with the LSTM cells with peephole connections and the COCOB optimizer, fed with deseasonalized data in a moving window format can be a competitive model generally across many datasets.' The qualification that carries the argument is the seasonality rule: RNNs can model seasonality directly when the series in a dataset have homogeneous seasonal patterns and comparable aligned lengths, but when patterns differ they should be deseasonalized first. Comparisons on six datasets show the recommended RNN configuration outperforming both automatic ETS and ARIMA on some sets (notably CIF 2016, NN5 and Wikipedia web traffic), being competitive on M3, and losing to ARIMA on the full M4 monthly set. The authors read this as evidence that cross-series training plus RNN capacity helps on many, but not all, forecasting problems.

Load-bearing premise

The rankings assume that 50 iterations of automated tuning with the same starting ranges are enough to give every model an equal chance; if that budget favours some architectures, the recommended recipe could change.

Editorial extensions

If this is right

  • A practitioner with a large database of related series can use the paper's recipe—stacked LSTM with peepholes, COCOB, moving windows, deseasonalization—and expect accuracy competitive with automatic ETS and ARIMA, with better results on some datasets.
  • The seasonality rule gives a concrete test: if the series share the same seasonality and aligned dates, leave seasonality in the data; otherwise remove it with STL decomposition first.
  • The results imply that global models trained across many series are a genuine source of gains, since pooled linear regressions also improve when given longer lags and RNNs improve further on top of that in many datasets.
  • Because the decoder-based sequence-to-sequence model performed worst, the paper's evidence points to direct multi-horizon outputs (dense layer or stacked multiple output) rather than autoregressive decoding for RNN forecasting.
  • The cost analysis shows RNNs are feasible despite extra compute because most time goes into hyperparameter tuning, not the final training run; a tuned global model is comparable to per-series benchmarks on a per-series basis.

Reading between the lines

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

  • Beyond the paper: if the 50-iteration tuning budget is the deciding factor, then small changes in that budget could alter the recommended combination, so any practitioner should re-run a modest amount of hyperparameter search rather than adopt the recipe blindly.
  • Beyond the paper: the seasonality rule likely transfers to other deep forecasting architectures because it reflects how gradient-trained networks handle recurring patterns; testing convolutional or attention-based forecasters on the same datasets would settle that.
  • Beyond the paper: the decoder's error-accumulation problem suggests that probabilistic forecasting models built on direct output distributions may be easier to automate than sequential sampling decoders; this extends the paper's point-forecast evidence.
  • Beyond the paper: because the paper deliberately leaves out competition-winning hybrid models, its recommended recipe is a lower bound on what expert-tuned RNNs can achieve; the gap points to where future automation efforts should concentrate.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. This paper presents a broad empirical study of recurrent neural network architectures for univariate time series forecasting, accompanied by an open-source software framework. The authors implement 36 RNN variants combining three recurrent units (Elman, GRU, LSTM with peepholes), four architecture/input-format configurations (Stacked, S2S with decoder, S2S with dense layer and moving window, and S2S with dense layer without moving window), and three optimizers (Adam, Adagrad, COCOB). These are evaluated on six benchmark datasets (CIF 2016, NN5, M3 monthly, M4 monthly, Wikipedia web traffic, Tourism) against auto.arima, ets, and pooled/unpooled regression benchmarks, using SMAPE and MASE with fixed-origin validation, 10-seed ensembling, and nonparametric statistical tests. On the basis of the results, the paper proposes best-practice guidelines: the Stacked architecture with LSTM peephole cells and the COCOB optimizer is recommended as generally competitive; deseasonalization is recommended unless the series have homogeneous seasonal patterns; and moving-window input with a window size larger than the seasonal period is preferred. The paper concludes that RNNs are now a viable, competitive option for practitioners, while acknowledging that they are not a silver bullet and are computationally more expensive than classical benchmarks.

Significance. If its conclusions hold, the paper would provide useful, actionable guidance for practitioners and a reusable framework for future comparisons, both of which are valuable to the forecasting community. The study is carefully designed in several respects: it uses fixed-origin validation, multiple random seeds with median ensembling, a large and diverse set of datasets, nonparametric statistical tests, and it releases the implementation publicly. The broad finding that semi-automatic RNN models are competitive with ETS and ARIMA on some datasets is supported by Table 5 and is consistent with the recent trajectory of the field. The more specific recommendations, however, are weakened by the fact that the key distinctions among recurrent units and optimizers are not statistically significant (Friedman p-values of 0.101, 0.115, and 0.174 in Sections 5.2, 5.3, and 5.8), and by the lack of evidence that the 50-iteration SMAC budget in Section 4.3.1 is sufficient or fair across models. The paper is therefore significant as an engineering and empirical resource, but its prescriptive conclusions need to be either supported by additional evidence or substantially softened.

major comments (3)
  1. [§4.3.1, §5.10, §4.4] The 50-iteration SMAC budget is a load-bearing element for the paper's specific recommendations. The hyperparameter search in Section 4.3.1 tunes seven to nine continuous and integer hyperparameters over the Table 4 ranges, yet Section 5.10 justifies 50 iterations only by saying it was chosen to be 'suitable across all the datasets,' without reporting convergence diagnostics or comparing against a larger budget. Hyperparameters are selected from a single SMAC run and are not re-tuned for the ten seeds used in Section 4.4, so tuning noise propagates directly into the architecture, recurrent-unit, and optimizer rankings in Sections 5.1-5.3. Because a fixed budget of 50 evaluations in a 7-9 dimensional space may favor models with simpler loss surfaces or fewer parameters, the claim that Stacked LSTM COCOB is the recommended configuration needs either a sensitivity analysis (e.g., tuning curves, repeated tuning with different SMAC seeds, or a larger budget on a subset of datasets) or a more conditional framing.
  2. [§5.2, §5.3, §5.8, §6] The paper's central recommendations for the LSTM peephole cell and the COCOB optimizer are based on differences that the paper itself reports as not statistically significant: Friedman p-values of 0.101 for recurrent units in Section 5.2, 0.115 for optimizers in Section 5.3, and 0.174 in the parameter-count experiment in Section 5.8. The conclusion in Section 6 nonetheless recommends Stacked LSTM COCOB as a generally competitive model, even adding that LSTM is best 'though not statistically significant.' A non-significant ranking provides only weak evidence for a best-practice guideline. The authors should either temper these conclusions to say that no clear winner among units or optimizers can be established, or provide additional support such as effect sizes, consistent per-dataset directions, or a meta-analysis across datasets. Without this, the prescriptive claims in the abstract and conclusion outrun the statistical evidence.
  3. [§5.6, Table 6, §6] The seasonality guideline is partially supported but is stated too strongly for the NN5 dataset. Table 6 shows that the difference between using and not using STL decomposition on NN5 is not significant (p = 0.911), with average rankings of 1.56 vs. 1.44; only Wikipedia Web Traffic shows a significant advantage for no deseasonalization (p = 0.028). The conclusion in Section 6 that RNNs 'are capable of capturing the seasonality without prior deseasonalization' when series have homogeneous seasonal patterns is therefore not directly established by the NN5 result, which at most shows no significant disadvantage. The claim should be reformulated as a hypothesis or as evidence of non-inferiority under homogeneous seasonality, supported by the additional seasonal-pattern analysis in Section 5.6, rather than as a firm guideline.
minor comments (5)
  1. [§3.1.1, after Eq. (3e)] 'Hudmard Product' should be 'Hadamard Product'.
  2. [§5.3] 'Eventhough' should be 'Even though'.
  3. [§5.1] 'significanlty' should be 'significantly'.
  4. [§4.4.2, Table 5] SMAPE and MASE results are reported as point estimates only; given the emphasis on statistical testing and multi-seed ensembling, reporting confidence intervals for the final metrics, or at least for the key comparisons, would make the uncertainty visible to readers.
  5. [§4.3.1, Table 4] The presentation of Table 4 is cramped and makes it difficult to associate each hyperparameter column with the corresponding range; a reorganized table with clearly separated columns for each hyperparameter per dataset would improve readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper's conclusions are empirical summaries of external benchmark experiments, not derivations from fitted inputs.

full rationale

This paper is an empirical benchmarking study, not a derivation chain. The central claims—that the Stacked architecture with LSTM peephole cells and COCOB is competitive, and that RNNs can model homogeneous seasonality directly—are presented as observations from experiments on public datasets and are evaluated against the external benchmarks ets and auto.arima. No prediction is obtained by construction from a fitted parameter, and no model output is defined in terms of the quantity it is claimed to predict. The paper does cite the authors' prior work (Bandara et al., 2020) for the stacked architecture and preprocessing ideas, but these citations motivate methodological choices rather than supply the empirical conclusions; the comparisons in Section 5 and Table 5 are external and falsifiable. The statistical non-significance of some comparisons and the fixed 50-iteration SMAC tuning budget are legitimate concerns about robustness and fairness, but they are correctness risks, not circularity. The paper is self-contained against external benchmarks, so the appropriate finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper's contribution is empirical, so the ledger contains experimental heuristics and domain assumptions rather than fitted theoretical parameters. The most consequential choices are the 1.25 window rule and the 50-iteration SMAC budget.

free parameters (4)
  • Input window multiplier = 1.25
    Heuristic constant (Section 4.2.5) used to set the input window size relative to the output window size or seasonality period.
  • SMAC tuning iterations = 50
    Number of hyperparameter evaluations used for every model (Section 5.10); a larger budget could change model rankings.
  • SMAPE epsilon = 0.1
    Constant added in the modified SMAPE to avoid division by zero (Section 4.4.2), adopted from Suilin (2017).
  • Significance level = 0.05
    Threshold for Friedman, Hochberg, and Wilcoxon tests (Section 4.6).
assumptions (4)
  • standard math RNNs are universal approximators capable of modelling nonlinear relationships
    Invoked in Section 2.3 citing Hornik et al. (1989), underlying the expectation that RNNs can approximate the target function F in Eq. (1).
  • domain assumption Global models trained across related series can improve forecasts
    Core premise of the experimental design (Section 4.3), relying on series similarity within each dataset; the paper acknowledges this works only for homogeneous series.
  • domain assumption The selected datasets are representative of forecasting practice
    Used in Section 5 to generalize conclusions; the paper uses six competition datasets, but notes limitations such as single seasonality, univariate, and fixed-origin evaluation.
  • domain assumption Homogeneous seasonal patterns can be judged from seasonality strength plots
    In Section 5.6, the seasonality modelling conclusion is based on visual pattern plots and strength violin plots rather than a formal homogeneity test.

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Cite this review

Pith. "Pith review of Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions." pith.science (2026). https://pith.science/paper/66ETS4PT

@misc{pith2026190900590,
  author       = {Pith},
  title        = {Pith review of: Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/66ETS4PT}},
  note         = {Machine review of arXiv:1909.00590}
}
read the original abstract

Recurrent Neural Networks (RNN) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition. However, established statistical models such as ETS and ARIMA gain their popularity not only from their high accuracy, but they are also suitable for non-expert users as they are robust, efficient, and automatic. In these areas, RNNs have still a long way to go. We present an extensive empirical study and an open-source software framework of existing RNN architectures for forecasting, that allow us to develop guidelines and best practices for their use. For example, we conclude that RNNs are capable of modelling seasonality directly if the series in the dataset possess homogeneous seasonal patterns, otherwise we recommend a deseasonalization step. Comparisons against ETS and ARIMA demonstrate that the implemented (semi-)automatic RNN models are no silver bullets, but they are competitive alternatives in many situations.

Figures

Figures reproduced from arXiv: 1909.00590 by the authors.

Figure 1
Figure 1. Elman Recurrent Unit ht = σ(Wi · ht−1 + Vi · xt + bi) (2a) zt = tanh(Wo · ht + bo) (2b) In Equations 2a and 2b, ht ∈ R d denotes the hidden state of the RNN cell (d being the cell dimension). This is the only form of memory in the ERNN cell. xt ∈ R m (m being the size of the input) and zt ∈ R d denote the input and output of the cell at time step t. Wi ∈ R d×d and Vi ∈ R d×d denote the weight matrices whereas bi ∈ R… view at source ↗
Figure 2
Figure 2. Basic Long Short-Term Memory Unit it = σ(Wi · ht−1 + Vi · xt + bi) (3a) ot = σ(Wo · ht−1 + Vo · xt + bo) (3b) ft = σ(Wf · ht−1 + Vf · xt + bf ) (3c) C˜ t = tanh(Wc · ht−1 + Vc · xt + bc) (3d) Ct = it C˜ t + ft Ct−1 (3e) ht = ot tanh(Ct) (3f) zt = ht (3g) Compared to the basic RNN cell, the LSTM cell has two components to its state, the hidden state and the internal cell state where the hidden state corresponds to th… view at source ↗
Figure 3
Figure 3. Gated Recurrent Unit ut = σ(Wu · ht−1 + Vu · xt + bu) (5a) rt = σ(Wr · ht−1 + Vr · xt + br) (5b) h˜ t = tanh(Wh · rt · ht−1 + Vh · xt + bh) (5c) ht = ut h˜ t + (1 − ut) ht−1 (5d) zt = ht (5e) ut, rt ∈ R d denote the update and reset gates respectively. h˜ t ∈ R d indicates the candidate hidden state and ht ∈ R d indicates the current hidden state at time step t. The weights and biases follow the same notation as men… view at source ↗
Figures from the paper (20 more)
Figure 4
Figure 4. Figure 4: Folded Version of RNN [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Stacked Architecture of stacking means that multiple LSTM layers can be stacked on top of one another. In the most basic setup, the model has only one LSTM layer [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: Multi-layer Stacked Architecture During the model training process, the error is calculated per each time step and accumulated until the end of the time series. Let the error per each time step t be et. Then, et = Yt − Yˆ t (6) where Yt is the actual output vector at t…
Figure 7
Figure 7. Figure 7: Sequence to Sequence with Decoder Architecture [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]
Figure 8
Figure 8. Figure 8: Sequence to Sequence with Dense Layer Architecture [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 9
Figure 9. Figure 9: Violin Plots of Seasonality Strengths. The two datasets NN5 and Tourism have higher seasonality strength [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]
Figure 10
Figure 10. Figure 10: Train Validation Set Split However, as stated by Suilin (2017), this kind of split is problematic since the last part of the sequence is not considered for training the model. The further away the test predictions are from the training set the worse, since the underly…
Figure 11
Figure 11. Figure 11: Moving Window Scheme Even though the validation stage does not involve explicit training of the model, the created blocks should still be fed in sequence to build up the state. The output window size is set to be equal to the size of the forecasting horizon H (n = H).…
Figure 12
Figure 12. Figure 12: Relative Performance of Different RNN Architectures [PITH_FULL_IMAGE:figures/full_fig_p034_12.png]
Figure 13
Figure 13. Figure 13: Relative Performance of Different Recurrrent Cell Types [PITH_FULL_IMAGE:figures/full_fig_p035_13.png]
Figure 14
Figure 14. Figure 14: Relative Performance of Different Optimizers [PITH_FULL_IMAGE:figures/full_fig_p036_14.png]
Figure 15
Figure 15. Figure 15: Comparison of the Ouput Component for the Sequence to Sequence with the Dense Layer Architecture [PITH_FULL_IMAGE:figures/full_fig_p036_15.png]
Figure 16
Figure 16. Figure 16: Comparison of Input Window Sizes for the Stacked Architecture [PITH_FULL_IMAGE:figures/full_fig_p037_16.png]
Figure 17
Figure 17. Figure 17: Comparison of the Performance with and without STL Decomposition - Mean SMAPE [PITH_FULL_IMAGE:figures/full_fig_p038_17.png]
Figure 18
Figure 18. Figure 18: Comparison of the Performance with and without STL Decomposition - Mean MASE [PITH_FULL_IMAGE:figures/full_fig_p039_18.png]
Figure 19
Figure 19. Figure 19: Seasonal Patterns of All the Datasets 41 [PITH_FULL_IMAGE:figures/full_fig_p041_19.png]
Figure 20
Figure 20. Figure 20: Performance of RNNs Compared to Traditional Univariate Techniques - Mean SMAPE [PITH_FULL_IMAGE:figures/full_fig_p043_20.png]
Figure 21
Figure 21. Figure 21: Performance of RNNs Compared to Traditional Univariate Techniques - Median SMAPE [PITH_FULL_IMAGE:figures/full_fig_p044_21.png]
Figure 22
Figure 22. Figure 22: Performance of RNNs Compared to Traditional Univariate Techniques in Different M4 Categories - Mean [PITH_FULL_IMAGE:figures/full_fig_p045_22.png]
Figure 23
Figure 23. Figure 23: Relative Performance of Different Recurrrent Cell Types under the Same Number of Total Trainable [PITH_FULL_IMAGE:figures/full_fig_p046_23.png]

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Reference graph

Works this paper leans on

103 extracted references · 71 canonical work pages

  1. [1]

    Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Man\' e , D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., T...

    2015

  2. [2]

    C., Rangapuram, S

    Alexandrov, A., Benidis, K., Bohlke - Schneider, M., Flunkert, V., Gasthaus, J., Januschowski, T., Maddix, D. C., Rangapuram, S. S., Salinas, D., Schulz, J., Stella, L., T \" u rkmen, A. C., Wang, Y., 2019. Gluonts: Probabilistic time series models in python. CoRR abs/1906.05264. ://arxiv.org/abs/1906.05264

    arXiv 2019

  3. [3]

    Assaad, M., Bon \'e , R., Cardot, H., Jan. 2008. A new boosting algorithm for improved time-series forecasting with recurrent neural networks. Inf. Fusion 9 (1), 41--55

    2008

  4. [4]

    The tourism forecasting competition

    Athanasopoulos, G., Hyndman, R., Song, H., Wu, D., 2011. The tourism forecasting competition. International Journal of Forecasting 27 (3), 822 -- 844

    2011

  5. [5]

    J., Song, H., Wu, D., 2010

    Athanasopoulos, G., Hyndman, R. J., Song, H., Wu, D., 2010. Tourism forecasting part two. ://www.kaggle.com/c/tourism2/data

    2010

  6. [6]

    Neural machine translation by jointly learning to align and translate

    Bahdanau, D., Cho, K., Bengio, Y., 2015. Neural machine translation by jointly learning to align and translate. In: Bengio, Y., LeCun, Y. (Eds.), 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings. ://arxiv.org/abs/1409.0473

    arXiv 2015

  7. [7]

    Z., Koltun, V., 2018

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

    arXiv 2018

  8. [8]

    Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach

    Bandara, K., Bergmeir, C., Smyl, S., 2020. Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach. Expert Systems with Applications 140, 112896

    2020

Show all 103 references
  1. [9]

    Sales demand forecast in e-commerce using a long short-term memory neural network methodology

    Bandara, K., Shi, P., Bergmeir, C., Hewamalage, H., Tran, Q., Seaman, B., 2019. Sales demand forecast in e-commerce using a long short-term memory neural network methodology. In: Gedeon, T., Wong, K. W., Lee, M. (Eds.), Neural Information Processing. Springer International Pub...

    2019

  2. [10]

    Learning stochastic recurrent networks

    Bayer, J., Osendorfer, C., 2014. Learning stochastic recurrent networks. ://arxiv.org/abs/1411.7610

    2014 arXiv

  3. [11]

    A review and comparison of strategies for multi-step ahead time series forecasting based on the nn5 forecasting competition

    Ben Taieb , S., Bontempi, G., Atiya, A., Sorjamaa, A., 6 2012. A review and comparison of strategies for multi-step ahead time series forecasting based on the nn5 forecasting competition. Expert Systems with Applications 39 (8), 7067--7083

    2012

  4. [12]

    J., Koo, B., 2018

    Bergmeir, C., Hyndman, R. J., Koo, B., 2018. A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics & Data Analysis 120, 70 -- 83

    2018

  5. [13]

    Hyperopt: Distributed asynchronous hyper-parameter optimization

    Bergstra, J., 2012. Hyperopt: Distributed asynchronous hyper-parameter optimization. ://github.com/hyperopt/hyperopt

    2012

  6. [14]

    Random search for Hyper-Parameter optimization

    Bergstra, J., Bengio, Y., 2012. Random search for Hyper-Parameter optimization. J. Mach. Learn. Res. 13 (Feb), 281--305

    2012

  7. [15]

    M., Maiorino, E., Kampffmeyer, M

    Bianchi, F. M., Maiorino, E., Kampffmeyer, M. C., Rizzi, A., Jenssen, R., 2017. An overview and comparative analysis of recurrent neural networks for short term load forecasting. CoRR abs/1705.04378. ://arxiv.org/abs/1705.04378

    2017 arXiv

  8. [16]

    W., 2018

    Borovykh, A., Bohte, S., Oosterlee, C. W., 2018. Conditional time series forecasting with convolutional neural networks. arXiv preprint arXiv:1703.04691. ://arxiv.org/abs/1703.04691

    2018 arXiv

  9. [17]

    Time Series Analysis: Forecasting and Control

    Box, G., Jenkins, G., Reinsel, G., 1994. Time Series Analysis: Forecasting and Control. Forecasting and Control Series. Prentice Hall

    1994

  10. [18]

    Chen, C., Twycross, J., Garibaldi, J. M., Mar. 2017. A new accuracy measure based on bounded relative error for time series forecasting. PLoS One 12 (3), e0174202

    2017

  11. [19]

    Learning phrase representations using RNN Encoder--Decoder for statistical machine translation

    Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y., 25--29 October 2014. Learning phrase representations using RNN Encoder--Decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in ...

    2014

  12. [20]

    G., Mirisaee, H., Goswami, P., Gaussier, E., A \"i t-Bachir, A., Strijov, V., 2017

    Cinar, Y. G., Mirisaee, H., Goswami, P., Gaussier, E., A \"i t-Bachir, A., Strijov, V., 2017. Position-based content attention for time series forecasting with sequence-to-sequence rnns. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S. M. (Eds.), Neural Information Proce...

    2017

  13. [21]

    Claveria, O., Monte, E., Torra, S., Sep. 2017. Data pre-processing for neural network-based forecasting: does it really matter? Technological and Economic Development of Economy 23 (5), 709--725

    2017

  14. [22]

    Claveria, O., Torra, S., Jan. 2014. Forecasting tourism demand to catalonia: Neural networks vs. time series models. Econ. Model. 36, 220--228

    2014

  15. [23]

    B., Cleveland, W

    Cleveland, R. B., Cleveland, W. S., McRae, J. E., Terpenning, I., Jan. 1990. STL : A Seasonal-Trend decomposition procedure based on loess. J. Off. Stat. 6 (1), 3--33

    1990

  16. [24]

    Capacity and trainability in recurrent neural networks

    Collins, J., Sohl-Dickstein, J., Sussillo, D., 2016. Capacity and trainability in recurrent neural networks. In: International Conference on Learning Representations 2016 (ICLR 2016)

    2016

  17. [25]

    F., 2008

    Crone, S. F., 2008. NN5 competition. ://www.neural-forecasting-competition.com/NN5/

    2008

  18. [26]

    F., Hibon, M., Nikolopoulos, K., Jul

    Crone, S. F., Hibon, M., Nikolopoulos, K., Jul. 2011. Advances in forecasting with neural networks? empirical evidence from the NN3 competition on time series prediction. Int. J. Forecast. 27 (3), 635--660

    2011

  19. [27]

    V., Langmore, I., Tran, D., Brevdo, E., Vasudevan, S., Moore, D., Patton, B., Alemi, A., Hoffman, M

    Dillon, J. V., Langmore, I., Tran, D., Brevdo, E., Vasudevan, S., Moore, D., Patton, B., Alemi, A., Hoffman, M. D., Saurous, R. A., 2017. Tensorflow distributions. CoRR abs/1711.10604. ://arxiv.org/abs/1711.10604

    2017 arXiv

  20. [28]

    Adaptive subgradient methods for online learning and stochastic optimization

    Duchi, J., Hazan, E., Singer, Y., 2011. Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12 (Jul), 2121--2159

    2011

  21. [29]

    Elman, J. L., Apr. 1990. Finding structure in time. Cogn. Sci. 14 (2), 179--211

    1990

  22. [30]

    M3 user guide

    eResearch Centre., M., 2019. M3 user guide. ://docs.massive.org.au/index.html

    2019

  23. [31]

    Bayesian optimization

    Fernando, 2012. Bayesian optimization. ://github.com/fmfn/BayesianOptimization

    2012

  24. [32]

    Regularization paths for generalized linear models via coordinate descent

    Friedman, J., Hastie, T., Tibshirani, R., 2010. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33 (1), 1--22

    2010

  25. [33]

    Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power

    Garc \' a, S., Fern \'a ndez, A., Luengo, J., Herrera, F., May 2010. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf. Sci. 180 (10), 2044--2064

    2010

  26. [34]

    S., Salinas, D., Flunkert, V., Januschowski, T., 16--18 Apr 2019

    Gasthaus, J., Benidis, K., Wang, Y., Rangapuram, S. S., Salinas, D., Flunkert, V., Januschowski, T., 16--18 Apr 2019. Probabilistic forecasting with spline quantile function rnns. In: Chaudhuri, K., Sugiyama, M. (Eds.), Proceedings of Machine Learning Research. Vol. 89 of Proc...

    2019

  27. [35]

    Web traffic time series forecasting

    Google, 2017. Web traffic time series forecasting. ://www.kaggle.com/c/web-traffic-time-series- forecasting

    2017

  28. [36]

    Deep residual learning for image recognition

    He , K., Zhang , X., Ren , S., Sun , J., 27--30 June 2016. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 770--778

    2016

  29. [37]

    Hochreiter, S., Schmidhuber, J., Nov. 1997. Long short-term memory. Neural Comput. 9 (8), 1735--1780

    1997

  30. [38]

    Hornik, K., Stinchcombe, M., White, H., Jan. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2 (5), 359--366

    1989

  31. [39]

    H., Leyton-Brown, K., 17--21 Jan 2011

    Hutter, F., Hoos, H. H., Leyton-Brown, K., 17--21 Jan 2011. Sequential model-based optimization for general algorithm configuration. In: Coello, C. A. C. (Ed.), Learning and Intelligent Optimization. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 507--523

    2011

  32. [40]

    A brief history of time series forecasting competitions

    Hyndman, R., 2018. A brief history of time series forecasting competitions. ://robjhyndman.com/hyndsight/forecasting-competitions/

    2018

  33. [41]

    tsfeatures: Time Series Feature Extraction

    Hyndman, R., Kang, Y., Talagala, T., Wang, E., Yang, Y., 2019. tsfeatures: Time Series Feature Extraction. R package version 1.0.0. ://pkg.robjhyndman.com/tsfeatures/

    2019

  34. [42]

    Automatic time series forecasting: The forecast package for R

    Hyndman, R., Khandakar, Y., 2008. Automatic time series forecasting: The forecast package for R . Journal of Statistical Software, Articles 27 (3), 1--22

    2008

  35. [43]

    Forecasting with exponential smoothing

    Hyndman, R., Koehler, A., Ord, K., D Snyder, R., 01 2008. Forecasting with exponential smoothing. The state space approach. Springer Berlin Heidelberg

    2008

  36. [44]

    J., Koehler, A

    Hyndman, R. J., Koehler, A. B., Oct. 2006. Another look at measures of forecast accuracy. Int. J. Forecast. 22 (4), 679--688

    2006

  37. [45]

    Batch normalization: Accelerating deep network training by reducing internal covariate shift

    Ioffe, S., Szegedy, C., 06--11 July 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning - Volume 37. ICML'15. JMLR.org, pp. 448--456

    2015

  38. [46]

    N., Yu, H., Jun

    Jagannatha, A. N., Yu, H., Jun. 2016. Bidirectional RNN for medical event detection in electronic health records. Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting 2016, 473--482

    2016

  39. [47]

    Criteria for classifying forecasting methods

    Januschowski, T., Gasthaus, J., Wang, Y., Salinas, D., Flunkert, V., Bohlke-Schneider, M., Callot, L., 2020. Criteria for classifying forecasting methods. International Journal of Forecasting 36 (1), 167 -- 177, m4 Competition

    2020

  40. [48]

    Ji, Y., Haffari, G., Eisenstein, J., Jun. 2016. A latent variable recurrent neural network for discourse-driven language models. In: Proceedings of the 2016 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language Technologies....

    2016

  41. [49]

    An empirical exploration of recurrent network architectures

    Jozefowicz, R., Zaremba, W., Sutskever, I., 06--11 July 2015. An empirical exploration of recurrent network architectures. In: Proceedings of the 32nd International Conference on Machine Learning - Volume 37. ICML'15. JMLR.org, pp. 2342--2350

    2015

  42. [50]

    P., Ba, J., 7--9 May 2015

    Kingma, D. P., Ba, J., 7--9 May 2015. Adam: A method for stochastic optimization. In: 3rd International Conference for Learning Representations. Vol. 1412

    2015

  43. [51]

    A clockwork RNN

    Koutn \' k, J., Greff, K., Gomez, F., Schmidhuber, J., 21--26 Jun 2014. A clockwork RNN . In: Proceedings of the 31st International Conference on Machine Learning - Volume 32. ICML'14. JMLR.org, pp. II--1863--II--1871

    2014

  44. [52]

    Ensembles of recurrent neural networks for robust time series forecasting

    Krstanovic, S., Paulheim, H., 12--14 Dec 2017. Ensembles of recurrent neural networks for robust time series forecasting. In: Artificial Intelligence XXXIV . Springer International Publishing, pp. 34--46

    2017

  45. [53]

    Modeling long- and Short-Term temporal patterns with deep neural networks

    Lai, G., Chang, W.-C., Yang, Y., Liu, H., 8--12 July 2018. Modeling long- and Short-Term temporal patterns with deep neural networks. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. SIGIR '18. ACM, pp. 95--104

    2018

  46. [54]

    E., Smyl, S., 06--11 Aug 2017

    Laptev, N., Yosinski, J., Li, L. E., Smyl, S., 06--11 Aug 2017. Time-series extreme event forecasting with neural networks at uber. In: International Conference on Machine Learning. Vol. 34. pp. 1--5

    2017

  47. [55]

    Geoman: Multi-level attention networks for geo-sensory time series prediction

    Liang, Y., Ke, S., Zhang, J., Yi, X., Zheng, Y., 13--19 July 2018. Geoman: Multi-level attention networks for geo-sensory time series prediction. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18 . International Joint Con...

    2018

  48. [56]

    Smac v3: Algorithm configuration in python

    Lindauer, M., Eggensperger, K., Feurer, M., Falkner, S., Biedenkapp, A., Hutter, F., 2017. Smac v3: Algorithm configuration in python. ://github.com/automl/SMAC3

    2017

  49. [57]

    D., Sep 19--21 2015

    Luong, T., Pham, H., Manning, C. D., Sep 19--21 2015. Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Stroudsburg, PA, USA...

    2015

  50. [58]

    Makridakis, S., Hibon, M., Oct. 2000. The M3-Competition : results, conclusions and implications. Int. J. Forecast. 16 (4), 451--476

    2000

  51. [59]

    Makridakis, S., Spiliotis, E., Assimakopoulos, V., Oct. 2018 a . The M4 competition: Results, findings, conclusion and way forward. Int. J. Forecast. 34 (4), 802--808

    2018

  52. [60]

    Statistical and machine learning forecasting methods: Concerns and ways forward

    Makridakis, S., Spiliotis, E., Assimakopoulos, V., mar 2018 b . Statistical and machine learning forecasting methods: Concerns and ways forward. PLOS ONE 13 (3), e0194889

    2018

  53. [61]

    Mandal, P., Senjyu, T., Urasaki, N., Funabashi, T., Jul. 2006. A neural network based several-hour-ahead electric load forecasting using similar days approach. Int. J. Electr. Power Energy Syst. 28 (6), 367--373

    2006

  54. [62]

    J., Talagala, T

    Montero-Manso, P., Athanasopoulos, G., Hyndman, R. J., Talagala, T. S., 2020. Fforma: Feature-based forecast model averaging. International Journal of Forecasting 36 (1), 86 -- 92, m4 Competition

    2020

  55. [63]

    Nelson, M., Hill, T., Remus, W., O'Connor, M., Sep. 1999. Time series forecasting using neural networks: Should the data be deseasonalized first? J. Forecast. 18 (5), 359--367

    1999

  56. [64]

    Orabona, F., 2017. cocob. ://github.com/bremen79/cocob

    2017

  57. [65]

    Training deep networks without learning rates through coin betting

    Orabona, F., Tommasi, T., 04--09 Dec 2017. Training deep networks without learning rates through coin betting. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS'17. Curran Associates Inc., USA, pp. 2157--2167

    2017

  58. [66]

    N., Carpov, D., Chapados, N., Bengio, Y., 2019

    Oreshkin, B. N., Carpov, D., Chapados, N., Bengio, Y., 2019. N-BEATS: neural basis expansion analysis for interpretable time series forecasting. CoRR abs/1905.10437. ://arxiv.org/abs/1905.10437

    2019 arXiv

  59. [67]

    Multi-step-ahead host load prediction with GRU based Encoder-Decoder in cloud computing

    Peng, C., Li, Y., Yu, Y., Zhou, Y., Du, S., 31 jan -- 03 Feb 2018. Multi-step-ahead host load prediction with GRU based Encoder-Decoder in cloud computing. In: 2018 10th International Conference on Knowledge and Smart Technology ( KST ). pp. 186--191

    2018

  60. [68]

    W., 2017

    Qin, Y., Song, D., Cheng, H., Cheng, W., Jiang, G., Cottrell, G. W., 2017. A dual-stage attention-based recurrent neural network for time series prediction. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. IJCAI’17. AAAI Press, p. 2627–2633

    2017

  61. [69]

    R: A Language and Environment for Statistical Computing

    R Core Team , 2014. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ://www.R-project.org/

    2014

  62. [70]

    M., Islam, M

    Rahman, M. M., Islam, M. M., Murase, K., Yao, X., Jan. 2016. Layered ensemble architecture for time series forecasting. IEEE Trans Cybern 46 (1), 270--283

    2016

  63. [71]

    S., Seeger, M., Gasthaus, J., Stella, L., Wang, Y., Januschowski, T., 2018

    Rangapuram, S. S., Seeger, M., Gasthaus, J., Stella, L., Wang, Y., Januschowski, T., 2018. Deep state space models for time series forecasting. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. NIPS'18. Curran Associates Inc., USA, ...

    2018

  64. [72]

    A., 2018

    Rob J Hyndman, G. A., 2018. Forecasting: Principles and Practice, 2nd Edition. OTexts. ://otexts.com/fpp2/

    2018

  65. [73]

    Deepar: Probabilistic forecasting with autoregressive recurrent networks

    Salinas, D., Flunkert, V., Gasthaus, J., Januschowski, T., 2019. Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting

    2019

  66. [74]

    M., Zimmermann, H

    Sch\" a fer, A. M., Zimmermann, H. G., 10--14 Sep 2006. Recurrent neural networks are universal approximators. In: Proceedings of the 16th International Conference on Artificial Neural Networks - Volume Part I. ICANN'06. Springer-Verlag, Berlin, Heidelberg, pp. 632--640

    2006

  67. [75]

    Schuster, M., Paliwal, K. K., Nov. 1997. Bidirectional recurrent neural networks. Trans. Sig. Proc. 45 (11), 2673--2681

    1997

  68. [76]

    Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting

    Sen, R., Yu, H.-F., Dhillon, I., 2019. Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting. ://arxiv.org/abs/1905.03806

    2019 arXiv

  69. [77]

    Sharda, R., Patil, R. B., Oct. 1992. Connectionist approach to time series prediction: an empirical test. J. Intell. Manuf. 3 (5), 317--323

    1992

  70. [78]

    Temporal pattern attention for multivariate time series forecasting

    Shih, S.-Y., Sun, F.-K., Lee, H.-y., Sep 2019. Temporal pattern attention for multivariate time series forecasting. Machine Learning 108 (8), 1421--1441

    2019

  71. [79]

    Forecasting short time series with LSTM neural networks

    Smyl, S., 2016. Forecasting short time series with LSTM neural networks. Accessed: 2018-10-30. ://gallery.azure.ai/Tutorial/Forecasting-Short-Time- Series-with-LSTM-Neural-Networks-2

    2016

  72. [80]

    Ensemble of specialized neural networks for time series forecasting

    Smyl, S., 25--28 jun 2017. Ensemble of specialized neural networks for time series forecasting. In: 37th International Symposium on Forecasting

    2017

  73. [81]

    A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting

    Smyl, S., 2020. A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting 36 (1), 75 -- 85, m4 Competition

    2020

  74. [82]

    Data preprocessing and augmentation for multiple short time series forecasting with recurrent neural networks

    Smyl, S., Kuber, K., 19--22 Jun 2016. Data preprocessing and augmentation for multiple short time series forecasting with recurrent neural networks. In: 36th International Symposium on Forecasting

    2016

  75. [83]

    Spearmint

    Snoek, J., 2012. Spearmint. ://github.com/JasperSnoek/spearmint

    2012

  76. [84]

    P., 03--08 Dec 2012

    Snoek, J., Larochelle, H., Adams, R. P., 03--08 Dec 2012. Practical bayesian optimization of machine learning algorithms. In: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 2. NIPS'12. Curran Associates Inc., USA, pp. 2951--2959

    2012

  77. [85]

    S., Gunasekar, S., Srebro, N., Jan

    Soudry, D., Hoffer, E., Nacson, M. S., Gunasekar, S., Srebro, N., Jan. 2018. The implicit bias of gradient descent on separable data. J. Mach. Learn. Res. 19 (1), 2822--2878

    2018

  78. [86]

    On the results and observations of the time series forecasting competition cif 2016

    S t e pni c ka, M., Burda, M., 09 --12 July 2017. On the results and observations of the time series forecasting competition cif 2016. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). pp. 1--6

    2017

  79. [87]

    kaggle-web-traffic

    Suilin, A., 2017. kaggle-web-traffic. Accessed: 2018-11-19. ://github.com/Arturus/kaggle-web-traffic/

    2017

  80. [88]

    V., 08--13 Dec 2014

    Sutskever, I., Vinyals, O., Le, Q. V., 08--13 Dec 2014. Sequence to sequence learning with neural networks. In: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2. NIPS'14. MIT Press, Cambridge, MA, USA, pp. 3104--3112

    2014

  81. [89]

    Tang, Z., de Almeida, C., Fishwick, P. A., Nov. 1991. Time series forecasting using neural networks vs. box- jenkins methodology. Simulation 57 (5), 303--310

    1991

  82. [90]

    R., Kourentzes, N., Fildes, R., Feb 2015

    Trapero, J. R., Kourentzes, N., Fildes, R., Feb 2015. On the identification of sales forecasting models in the presence of promotions. Journal of the Operational Research Society 66 (2), 299--307

    2015

  83. [91]

    W., Kavukcuoglu, K., Sep 13-15 2016

    van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. W., Kavukcuoglu, K., Sep 13-15 2016. Wavenet: A generative model for raw audio. In: The 9th ISCA Speech Synthesis Workshop. ISCA , p. 125

    2016

  84. [92]

    Deep factors for forecasting

    Wang, Y., Smola, A., Maddix, D., Gasthaus, J., Foster, D., Januschowski, T., 09--15 Jun 2019. Deep factors for forecasting. In: Chaudhuri, K., Salakhutdinov, R. (Eds.), Proceedings of the 36th International Conference on Machine Learning. Vol. 97 of Proceedings of Machine Lear...

    2019

  85. [93]

    A Multi-Horizon quantile recurrent forecaster

    Wen, R., Torkkola, K., Narayanaswamy, B., Madeka, D., 04 - 09 Dec 2017. A Multi-Horizon quantile recurrent forecaster. In: 31st Conference on Neural Information Processing Systems (NIPS 2017), Time Series Workshop

    2017

  86. [94]

    Yan, W., Jul. 2012. Toward automatic time-series forecasting using neural networks. IEEE Trans Neural Netw Learn Syst 23 (7), 1028--1039

    2012

  87. [95]

    rBayesianOptimization: Bayesian Optimization of Hyperparameters

    Yan, Y., 2016. rBayesianOptimization: Bayesian Optimization of Hyperparameters. R package version 1.1.0. ://CRAN.R-project.org/package=rBayesianOptimization

    2016

  88. [96]

    Depth-Gated LSTM

    Yao, K., Cohn, T., Vylomova, K., Duh, K., Dyer, C., 22 Jun -- 14 Aug 2015. Depth-Gated LSTM . 20th Jelinek Summer Workshop on Speech and Language Technology 2015

    2015

  89. [97]

    A., 2000

    Yeo, I.-K., Johnson, R. A., 2000. A new family of power transformations to improve normality or symmetry. Biometrika 87 (4), 954--959

    2000

  90. [98]

    Y., 1998

    Zhang, G., Eddy Patuwo, B., Hu, M. Y., 1998. Forecasting with artificial neural networks: The state of the art. Int. J. Forecast. 14, 35--62

    1998

  91. [99]

    Zhang, G. P., Jan. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159--175

    2003

  92. [100]

    P., Berardi, V

    Zhang, G. P., Berardi, V. L., Jun. 2001. Time series forecasting with neural network ensembles: an application for exchange rate prediction. J. Oper. Res. Soc. 52 (6), 652--664

    2001

  93. [101]

    P., Kline, D

    Zhang, G. P., Kline, D. M., Nov. 2007. Quarterly Time-Series forecasting with neural networks. IEEE Trans. Neural Netw. 18 (6), 1800--1814

    2007

  94. [102]

    P., Qi, M., Jan

    Zhang, G. P., Qi, M., Jan. 2005. Neural network forecasting for seasonal and trend time series. Eur. J. Oper. Res. 160 (2), 501--514

    2005

  95. [103]

    Deep and confident prediction for time series at uber

    Zhu, L., Laptev, N., 18 -- 21 nov 2017. Deep and confident prediction for time series at uber. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, pp. 103--110

    2017

Pith tools

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