Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

Tokenizing Stock Prices for Enhanced Multi-Step Forecast and Prediction

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read PCIE, a patched-channel transformer encoder, claims better multi-step stock price forecasts and predictions than four state-of-the-art baselines on two U.S. datasets, at horizons 10, 20, 40, and 60 trading days.

desk verdict A plausible channel-mixing patch model for stock forecasting, but the evidence base has an internal inconsistency that must be fixed before the SOTA claim is credible. read the letter →

arxiv 2504.17313 v1 pith:DQWTHDZZ submitted 2025-04-24 cs.CE q-fin.CP

Zhuohang Zhu , Haodong Chen , Qiang Qu , Xiaoming Chen , Vera Chung This is my paper · ORCID
classification cs.CEq-fin.CP
keywords stockpriceforecastingpredictionmulti-steptokenizationpatchingchannelmixingtransformerencodertimeseries
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 tries to establish that a single encoder model can handle both tasks that stock-price models usually split apart: forecasting future close prices and predicting future percentage changes, over horizons of 10, 20, 40, and 60 trading days. The model, PCIE, tokenizes each input channel into overlapping patches, embeds each patch with an adaptive temporal block, and mixes all channels before self-attention; the authors report the lowest MSE and MAE among the compared models on both datasets at every horizon. They also claim that adding percentage-change channels alongside raw prices improves every model, including the baselines. A sympathetic reader takes away that tokenization plus channel mixing is a workable route to longer-horizon financial forecasts.

What carries the argument

The load-bearing object is the tokenization pipeline: univariate patching divides each input channel into patches of length P=4 with stride S=1; the adaptive temporal learning block chooses between a shared linear map, per-series linear maps, or an MLP to embed each patch; channel mixing flattens the embedded patches from all channels and adds learnable position encoding, so each self-attention token carries cross-channel information; and a final flatten-plus-linear layer emits all future steps in one pass. The direct multi-step output is what avoids iterative error accumulation, and instance normalization plus the price/percentage-change input pair handles distribution shift.

What would settle it

Run the identical experiment with a strictly chronological split (first 70% of trading days for training, next 10% for validation, last 20% for testing) and recompute the table; if PCIE's margins over PatchTST shrink or reverse, the SOTA claim rested on leakage from overlapping patches across the split boundary.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that price series can be represented as cross-channel tokens, and that this representation is what makes multi-step stock forecasting and prediction work. PCIE reports the lowest MSE and MAE among the five compared models on US_71 and US_14L, for all forecast and prediction lengths in {10, 20, 40, 60}. The ablation study shows that removing the tokenization process increases error in nearly every setting, which the authors take as evidence that patching, adaptive temporal learning, and channel mixing together capture correlations between channels that univariate models miss.

Load-bearing premise

The central claim rests on the unstated premise that the 7:1:2 train/validation/test split is chronological and leakage-free, since the paper does not say the split respects time order and overlapping patches could let training and test samples share days.

Editorial extensions

If this is right

  • Multi-step forecasts of 10 to 60 trading days can be produced in a single forward pass, so early errors cannot compound through iterative decoding.
  • Adding percentage-change channels alongside raw prices improved every model tested, including the baselines, which suggests the preprocessing trick transfers beyond PCIE.
  • The ablation ties the performance gain to tokenization itself, so future models can adopt patching and channel mixing even if they keep a different backbone.
  • The reported table gives a concrete target: on US_71 and US_14L, any new method should beat PCIE's MSE and MAE at horizons 10, 20, 40, and 60 before claiming an improvement.

Reading between the lines

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

  • A testable extension would ablate channel mixing while keeping patching fixed: the paper's ablation removes the whole tokenization process, so it does not isolate which component drives the gain.
  • The price-plus-percentage-change input is effectively a differencing scheme; the same preprocessing could be applied to volatility, volume, or other financial series, and may benefit models beyond these baselines.
  • Because the paper reports point estimates without variance across random seeds, a natural next check is whether the margins over PatchTST persist across repeated runs and across different market regimes in the test period.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes PCIE, a Patched Channel Integration Encoder, for multi-step stock price forecasting (predicting future close prices) and prediction (predicting future price changes). The model applies univariate patching, an adaptive temporal learning block, channel mixing, and a channel-mixing self-attention encoder with direct multi-step output. The authors evaluate PCIE on two US stock datasets, US_71 and US_14L, across horizons of 10, 20, 40, and 60 days, comparing against PatchTST, D-Va, Autoformer, and Informer. Table 2 reports lower MSE and MAE for PCIE than all baselines, and the authors also claim a data-preprocessing method that mixes price and percentage-change channels improves performance across all models. An ablation study in Table 4 is presented as evidence that the tokenization process is responsible for the improvement.

Significance. If the quantitative claims are correct, the paper offers a practical improvement for multi-step financial forecasting, with a tokenization design that captures cross-channel correlations and a preprocessing augmentation that appears to generalize across baselines. The paper is entirely empirical, provides no code release or machine-checked derivations, and its main support is the comparative table. The claimed SOTA result and the tokenization contribution are precisely what the internal inconsistencies in Tables 2 and 4 put at risk, so the significance is conditional on the reported numbers being reproducible and internally consistent.

major comments (4)
  1. [Comparison of Table 2 and Table 4] The same PCIE configuration is reported with different values in Table 2 and Table 4. For US_14L prediction, Table 2 gives MSE/MAE for L=20, 40, and 60 as 1.5181/0.8601, 1.4746/0.8520, and 1.4611/0.8502, while Table 4 gives 1.5356/0.8683, 1.5129/0.8664, and 1.4801/0.8577 for the same PCIE model. The L=10 row matches in both tables, so the discrepancy is not a change in task definition. One of the two tables must be incorrect, or the runs differ in an undisclosed way such as seed, hyperparameters, or data split. Since Table 2 is the evidence for the headline SOTA claim and Table 4 is the evidence for the tokenization contribution, the two tables cannot both be right; please reconcile them and report which numbers are correct.
  2. [Sections 4.1, 3.3, and 4.3] Section 4.1 states only that data is split into training, validation, and testing with a ratio of 7:1:2, but it does not state that the split is chronological. This matters because the tokenization uses overlapping patches with stride S=1 (Section 3.3) and the input features include percentage changes computed from adjacent days (Section 3.1), so a random split would allow test windows to overlap in time with training windows. Such leakage would inflate the reported performance for PCIE and for the baselines. The paper must state explicitly whether the split respects time order; if it does not, the experiments must be rerun with a chronological split and the corrected results reported.
  3. [Section 4.4 and Table 2] All results are single-run point estimates with no error bars, confidence intervals, or significance tests. Several margins in Table 2 are small; for example, US_71 prediction at L=60 reports PCIE MSE 0.9983 versus D-Va 1.0054, and US_14L forecast at L=40 reports PCIE 0.5570 versus PatchTST 0.5705. Without repeated runs from different seeds or a paired significance test, the claim that PCIE outperforms the baselines is not statistically supported. Please report results over multiple seeds (at least 3 to 5) as mean plus/minus standard deviation, and where appropriate apply a paired test such as the Diebold-Mariano test for forecast comparisons.
  4. [Section 4.4 and Table 3] Table 3 reports a single percentage improvement for each dataset and model under 'mixing data', but the paper never defines how 'overall performance' is computed. It is not stated whether the percentage is averaged over all horizons, over MSE and MAE, or over forecasting and prediction tasks, or some weighted combination. Without this definition, the contribution of the proposed preprocessing method cannot be evaluated quantitatively. Please specify the aggregation formula and provide a per-task, per-horizon breakdown of the improvement.
minor comments (5)
  1. [Section 4.3] The statement that 'all the other parameters are tuned according to the characteristics of the dataset' is too vague; please list the final hyperparameters, including d_model, d_patch, number of attention heads, and number of encoder layers, for each dataset.
  2. [Section 3.7] Instance normalization is mentioned in one sentence but its exact placement in the pipeline is not described; please clarify for which components it is applied and whether it is used in both forecasting and prediction.
  3. [Abstract and Section 1] The distributional claims that forecasting targets follow a log-normal distribution and prediction targets follow a normal distribution are stated without empirical support or citations; please add evidence or references.
  4. [Section 4.5 and Table 4] The 'No Tokenization' condition is not described; please explain what replaces the tokenization process in the ablation, otherwise the comparison is difficult to interpret.
  5. [References] Reference [31] appears incomplete; it lacks publication venue, year, and page numbers. Please complete the bibliographic details.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: PCIE's performance claims rest on test-set comparisons against external baselines, not on a fitted parameter renamed as a prediction or on a self-citation chain.

full rationale

The paper is an empirical study. It proposes the PCIE architecture, a tokenization procedure, and a data-preprocessing choice, then evaluates them on two stock datasets against four external baselines (PatchTST, D-Va, Autoformer, Informer). No theoretical claim is derived from the result it is supposed to establish. The model is trained with MSE loss and tuned on a validation set, and the reported metric is on a test portion of each dataset. Hyperparameters are not fitted to the test set in a way that would make the test numbers forced by construction. The paper contains no self-citations that carry a load-bearing argument: the references to reversible instance normalization, direct multi-step forecasting, and attention mechanisms are to external prior work, and the model's components are not justified by invoking the paper's own conclusions. The adaptive temporal learning block is trained by backpropagation rather than defined in terms of the target. The claim that mixing price and price-change channels helps is itself tested across models rather than assumed. The noted internal inconsistency between Table 2 and Table 4 for the same PCIE configuration on US_14L prediction is a reproducibility or reporting concern, not a circularity. Similarly, the absence of an explicit statement that the 7:1:2 split is chronological could permit temporal leakage, but that is an experimental-validity concern, not a circular reduction of the prediction to its input. Accordingly, no circular step can be exhibited from the text, and the circularity score is 0.

Assumptions & free parameters 8 free parameters · 6 assumptions · 0 invented entities

The central claim is an empirical performance comparison, so the ledger is dominated by hand-chosen and tuned hyperparameters plus domain assumptions about the data, normalization, and evaluation metric. No new physical or theoretical entities are introduced.

free parameters (8)
  • patch length P = 4
    Chosen by hand to balance local temporal context and sequence reduction (Section 4.3).
  • stride S = 1
    Chosen by hand; gives heavy overlap between patches, increasing token count and compute (Section 3.3).
  • max learning rate = 0.0001
    Tuned via OneCycleLR on validation MSE (Section 4.3).
  • batch size = 16
    Set during hyperparameter tuning (Section 4.3).
  • max epochs = 50
    With early stopping patience 19 (Section 4.3).
  • latent dimension d_patch
    Not specified in the paper; the dimension of the ATL block output is a free architecture choice affecting the central result.
  • model dimension d_model
    Equals d_patch times channel count M (Eq. 5); value not reported.
  • number of attention heads and encoder layers
    Not reported, despite being core to the self-attention encoder.
assumptions (6)
  • standard math Scaled dot-product attention (Vaswani et al. 2017) is a suitable mechanism for capturing temporal dependencies in stock prices.
    Used without proof in Eq. 6-8; standard transformer machinery.
  • domain assumption Batch normalization performs better than layer normalization for time series data.
    Stated with citations [16,29] in Section 3.4; treated as a given.
  • domain assumption Instance normalization (Kim et al. 2021) mitigates distribution shift in stock price data.
    Invoked in Section 3.7 with citation [10]; no verification on these datasets beyond the reported results.
  • domain assumption The 71 and 14 selected stocks are representative of the U.S. market.
    Section 4.1 states the selection criteria but does not justify representativeness.
  • domain assumption Anomaly detection on Yahoo Finance data removes invalid data without introducing bias.
    Mentioned in Section 4.1 with no details.
  • domain assumption MSE is an appropriate loss and evaluation metric for stock price forecast and prediction.
    Used throughout; no alternative financial metrics considered.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Tokenizing Stock Prices for Enhanced Multi-Step Forecast and Prediction." pith.science (2026). https://pith.science/paper/DQWTHDZZ

@misc{pith2026250417313,
  author       = {Pith},
  title        = {Pith review of: Tokenizing Stock Prices for Enhanced Multi-Step Forecast and Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DQWTHDZZ}},
  note         = {Machine review of arXiv:2504.17313}
}
read the original abstract

Effective stock price forecasting (estimating future prices) and prediction (estimating future price changes) are pivotal for investors, regulatory agencies, and policymakers. These tasks enable informed decision-making, risk management, strategic planning, and superior portfolio returns. Despite their importance, forecasting and prediction are challenging due to the dynamic nature of stock price data, which exhibit significant temporal variations in distribution and statistical properties. Additionally, while both forecasting and prediction targets are derived from the same dataset, their statistical characteristics differ significantly. Forecasting targets typically follow a log-normal distribution, characterized by significant shifts in mean and variance over time, whereas prediction targets adhere to a normal distribution. Furthermore, although multi-step forecasting and prediction offer a broader perspective and richer information compared to single-step approaches, it is much more challenging due to factors such as cumulative errors and long-term temporal variance. As a result, many previous works have tackled either single-step stock price forecasting or prediction instead. To address these issues, we introduce a novel model, termed Patched Channel Integration Encoder (PCIE), to tackle both stock price forecasting and prediction. In this model, we utilize multiple stock channels that cover both historical prices and price changes, and design a novel tokenization method to effectively embed these channels in a cross-channel and temporally efficient manner. Specifically, the tokenization process involves univariate patching and temporal learning with a channel-mixing encoder to reduce cumulative errors. Comprehensive experiments validate that PCIE outperforms current state-of-the-art models in forecast and prediction tasks.

Figures

Figures reproduced from arXiv: 2504.17313 by the authors.

Figure 1
Figure 1. Tokenization Process [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. PCIE Model Overview In the subsequent stage, segments from each series are concatenated to facil￾itate channel mixing. Channel mixing integrates the information across different series, allowing the model to learn correlations across multiple series. This is particularly crucial in stock market analysis, where the target series is often influenced by the movements of other series within the same dataset. The concate… view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FinCast: A Foundation Model for Financial Time-Series Forecasting

    cs.LG 2025-08 conditional novelty 5.0 of 10

    FinCast, a 1B-parameter sparse-MoE transformer pretrained on 20B+ financial time points, reports 20% and 23% average MSE reductions over SOTA in zero-shot and supervised financial forecasting.

Reference graph

Works this paper leans on

32 extracted references · 20 canonical work pages · cited by 1 Pith paper

  1. [1]

    In: 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation

    Ariyo, A.A., Adewumi, A.O., Ayo, C.K.: Stock price prediction using the arima model. In: 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation. pp. 106–112. IEEE (2014)

    work page 2014

  2. [2]

    In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

    Chen, Y., Wei, Z., Huang, X.: Incorporating corporation relationship via graph convolutional neural networks for stock price prediction. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. pp. 1655–1658. CIKM ’18, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3269206.3269269

    arXiv 2018

  3. [3]

    In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (Jul 2020)

    Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (Jul 2020). https://doi.org/10.24963/ijcai.2020/640

    doi:10.24963/ijcai.2020/640 2020

  4. [4]

    https://doi.org/10.3233/FAIA230328 14 Z

    Fan, C., Lu, H., Huang, A.: A Novel Differentiable Rank Learning Method Towards Stock Movement Quantile Forecasting (09 2023). https://doi.org/10.3233/FAIA230328 14 Z. Zhu et al

    work page doi:10.3233/faia230328 2023

  5. [5]

    In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (Aug 2019)

    Feng, F., Chen, H., He, X., Ding, J., Sun, M., Chua, T.S.: Enhancing stock movement prediction with adversarial training. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (Aug 2019). https://doi.org/10.24963/ijcai.2019/810

    doi:10.24963/ijcai.2019/810 2019

  6. [6]

    ACM Transactions on Information Systems pp

    Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems pp. 1–30 (Apr 2019). https://doi.org/10.1145/3309547

    doi:10.1145/3309547 2019

  7. [7]

    In: Proceedings of the Second ACM International Conference on AI in Finance (Nov 2021)

    Feng, F., Wang, X., He, X., Ng, R., Chua, T.S.: Time horizon-aware modeling of financial texts for stock price prediction. In: Proceedings of the Second ACM International Conference on AI in Finance (Nov 2021). https://doi.org/10.1145/3490354.3494416

    arXiv 2021

  8. [8]

    A Machine Learning Model for Stock Market Prediction

    Hegazy, O., Soliman, O.S., Salam, M.A.: A machine learning model for stock market prediction. arXiv preprint arXiv:1402.7351 (2014)

    work page Pith review arXiv 2014

Show all 32 references
  1. [9]

    RePEc: Research Papers in Economics - RePEc,RePEc: Research Papers in Economics - RePEc (Jan 2017)

    Hu, Z., Liu, W., Bian, J., Liu, X., Liu, T.Y.: Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction. RePEc: Research Papers in Economics - RePEc,RePEc: Research Papers in Economics - RePEc (Jan 2017)

    2017

  2. [10]

    In: International Conference on Learning Representations (2021), https://openreview.net/forum?id=cGDAkQo1C0p

    Kim, T., Kim, J., Tae, Y., Park, C., Choi, J.H., Choo, J.: Reversible instance normalization for accurate time-series forecasting against distribution shift. In: International Conference on Learning Representations (2021), https://openreview.net/forum?id=cGDAkQo1C0p

    2021

  3. [11]

    In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

    Koa, K.J., Ma, Y., Ng, R., Chua, T.S.: Diffusion variational autoencoder for tackling stochasticity in multi-step regression stock price prediction. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. pp. 1087–1096 (2023)

    2023

  4. [12]

    arXiv preprint arXiv:1808.06226 (2018)

    Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018)

    2018 arXiv

  5. [13]

    Mathematical Problems in Engineering2016 (2016)

    Li, L., Leng, S., Yang, J., Yu, M., et al.: Stock market autoregressive dynamics: A multinational comparative study with quantile regression. Mathematical Problems in Engineering2016 (2016)

    2016

  6. [14]

    RePEc: Research Papers in Economics - RePEc,RePEc: Research Papers in Economics - RePEc (Jun 2021)

    Lin, H., Zhang, D., Liu, W., Bian, J.: Learning multiple stock trading patterns with temporal routing adaptor and optimal transport. RePEc: Research Papers in Economics - RePEc,RePEc: Research Papers in Economics - RePEc (Jun 2021)

    2021

  7. [15]

    International Journal of Business Forecasting and Marketing Intelligence9(2), 179–196 (2024)

    Lohan, S., Sidhu, A., Kakran, S.: The impact of investor’s attention on global stock market: Statistical review of literature. International Journal of Business Forecasting and Marketing Intelligence9(2), 179–196 (2024)

    2024

  8. [16]

    Nie, Y., Nguyen, N., Sinthong, P., Kalagnanam, J.: A time series is worth 64 words: Long-term forecasting with transformers (Nov 2022)

    2022

  9. [17]

    Omega33(6), 497–505 (2005)

    Pai, P.F., Lin, C.S.: A hybrid arima and support vector machines model in stock price forecasting. Omega33(6), 497–505 (2005)

    2005

  10. [18]

    https://doi.org/10.3233/FAIA230476

    Peng, H., Yang, J.: Stock Movement Prediction via Attention-Aware Multi-Order Relation Graph Neural Network (09 2023). https://doi.org/10.3233/FAIA230476

    2023 doi

  11. [19]

    International Journal of Forecasting pp

    Raunig, B.: The longer-horizon predictability of german stock market volatility. International Journal of Forecasting pp. 363–372 (Apr 2006). https://doi.org/10.1016/j.ijforecast.2005.11.003

    2006 doi

  12. [20]

    In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Jan 2020)

    Sawhney, R., Agarwal, S., Wadhwa, A., Shah, R.R.: Deep attentive learning for stock movement prediction from social media text and company correlations. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Jan 2020). https://doi.o...

    2020 doi

  13. [21]

    In: Proceedings of the Third ACM International Conference on AI in Finance (Nov 2022)

    Tuncer, T., Kaya, U., Sefer, E., Alacam, O., Hoser, T.: Asset price and direction prediction via deep 2d transformer and convolutional neural networks. In: Proceedings of the Third ACM International Conference on AI in Finance (Nov 2022). https://doi.org/10.1145/3533271.3561738

    2022

  14. [22]

    Advances in Neural Information Processing Systems30 (2017)

    Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in Neural Information Processing Systems30 (2017)

    2017

  15. [23]

    Journal of Multinational Financial Management17(3), 231–243 (2007)

    Verma, R., Verma, P.: Noise trading and stock market volatility. Journal of Multinational Financial Management17(3), 231–243 (2007)

    2007

  16. [24]

    Expert Systems with Applications208, 118128 (2022)

    Wang, C., Chen, Y., Zhang, S., Zhang, Q.: Stock market index prediction using deep transformer model. Expert Systems with Applications208, 118128 (2022)

    2022

  17. [25]

    Advances in Neural Information Processing Systems34, 22419–22430 (2021)

    Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems34, 22419–22430 (2021)

    2021

  18. [26]

    In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Jan 2018)

    Xu, Y., Cohen, S.B.: Stock movement prediction from tweets and historical prices. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Jan 2018). https://doi.org/10.18653/v1/p18-1183

    2018 doi

  19. [27]

    Proceedings of the AAAI Conference on Artificial Intelligence pp

    Yang, L., Li, J., Dong, R., Zhang, Y., Smyth, B.: Numhtml: Numeric-oriented hierarchical transformer model for multi-task financial forecasting. Proceedings of the AAAI Conference on Artificial Intelligence pp. 11604–11612 (Jul 2022). https://doi.org/10.1609/aaai.v36i10.21414

    2022 doi

  20. [28]

    Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 37, pp. 11121–11128 (2023)

    2023

  21. [29]

    In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (Aug 2021)

    Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., Eickhoff, C.: A transformer-based framework for multivariate time series representation learning. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (Aug 2021). https://doi.org/10.1145/...

    2021

  22. [30]

    In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Aug 2017)

    Zhang, L., Aggarwal, C., Qi, G.J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Aug 2017). https://doi.org/10.1145/3097983.3098117

    2017

  23. [31]

    Zhao, Y., Du, H., Liu, Y., Wei, S., Chen, X., Zhuang, F., Li, Q., Liu, J., Kou, G.: Stock movement prediction based on bi-typed hybrid-relational market knowledge graph via dual attention networks

  24. [32]

    In: Proceedings of the AAAI Conference on Artificial Intelligence

    Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 11106–11115 (2021)

    2021

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.