Time Series Forecasting

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

86 leaderboard tables shown for this task, 86 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 86 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

46 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 46 until expanded.

Subtasks archive 2025-07-28

6 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 714 papers with code (1,609 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Sequence to Sequence Learning with Neural Networks
  10 Sep 2014 74 repositories listed Syntology ran 11 of 25 samples · 14 unverified · 9 pointer-only (licence)
  Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.
• Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
  19 Dec 2019 36 repositories listed Syntology ran 5 of 10 samples · 5 unverified · 5 pointer-only (licence)
  Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i.
• Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
  21 Mar 2017 21 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.
• Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
  6 Jul 2017 19 repositories listed Syntology ran 17 of 35 samples · 18 unverified · 15 pointer-only (licence)
  Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
• DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
  13 Apr 2017 19 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Probabilistic forecasting, i.
• N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
  24 May 2019 18 repositories listed Syntology ran 10 of 39 samples · 29 unverified · 3 pointer-only (licence)
  We focus on solving the univariate times series point forecasting problem using deep learning.
• Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
  14 Dec 2020 14 repositories listed Syntology ran 62 of 76 samples · 14 unverified · 13 pointer-only (licence)
  Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning.
• iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
  10 Oct 2023 11 repositories listed Syntology ran 8 of 10 samples · 2 unverified
  These forecasters leverage Transformers to model the global dependencies over temporal tokens of time series, with each token formed by multiple variates of the same timestamp.
• Are Transformers Effective for Time Series Forecasting?
  26 May 2022 10 repositories listed Syntology ran 13 of 19 samples · 6 unverified · 13 pointer-only (licence)
  Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task.
• AA-Forecast: Anomaly-Aware Forecast for Extreme Events
  21 Aug 2022 9 repositories listed
  Moreover, the framework employs a dynamic uncertainty optimization algorithm that reduces the uncertainty of forecasts in an online manner.
• A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
  27 Nov 2022 8 repositories listed Syntology ran 7 of 30 samples · 23 unverified
  Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models.
• GluonTS: Probabilistic Time Series Models in Python
  12 Jun 2019 8 repositories listed Syntology ran 0 of 18 samples · 18 unverified
  We introduce Gluon Time Series (GluonTS, available at https://gluon-ts.
• Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
  14 Sep 2017 7 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Timely accurate traffic forecast is crucial for urban traffic control and guidance.
• SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
  17 Jun 2021 6 repositories listed Syntology ran 1 of 6 samples · 5 unverified
  One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences.
• Deep and Confident Prediction for Time Series at Uber
  6 Sep 2017 6 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing.
• Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
  9 Oct 2023 5 repositories listed Syntology ran 5 of 12 samples · 7 unverified · 4 pointer-only (licence)
  Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting have been proposed recently.
• PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
  30 May 2023 5 repositories listed Syntology ran 0 of 21 samples · 21 unverified
  PyPOTS is an open-source Python library dedicated to data mining and analysis on multivariate partially-observed time series, i.
• Long-term Forecasting with TiDE: Time-series Dense Encoder
  17 Apr 2023 5 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  Recent work has shown that simple linear models can outperform several Transformer based approaches in long term time-series forecasting.
• TSMixer: An All-MLP Architecture for Time Series Forecasting
  10 Mar 2023 5 repositories listed
  Extending them, in this paper, we investigate the capabilities of linear models for time-series forecasting and present Time-Series Mixer (TSMixer), a novel architecture designed by stacking multi-layer perceptrons…
• Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
  23 Jan 2020 5 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  In this paper, we present a new approach to time series forecasting.
• Probabilistic Forecasting with Temporal Convolutional Neural Network
  11 Jun 2019 5 repositories listed Syntology ran 0 of 8 samples · 8 unverified
  We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting.
• GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
  7 Mar 2019 5 repositories listed
  The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks.
• TSI-Bench: Benchmarking Time Series Imputation
  18 Jun 2024 4 repositories listed
  Despite the development of numerous deep learning algorithms for time series imputation, the community lacks standardized and comprehensive benchmark platforms to effectively evaluate imputation performance across…
• SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting
  22 Aug 2023 4 repositories listed
  To address these issues, we propose two novel strategies to reduce the number of iterations in RNNs for LTSF tasks: Segment-wise Iterations and Parallel Multi-step Forecasting (PMF).
• N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
  30 Jan 2022 4 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems.
• TS2Vec: Towards Universal Representation of Time Series
  19 Jun 2021 4 repositories listed Syntology ran 11 of 23 samples · 12 unverified
  Furthermore, to obtain the representation of an arbitrary sub-sequence in the time series, we can apply a simple aggregation over the representations of corresponding timestamps.
• Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm
  7 Jul 2019 4 repositories listed
  Due to their prevalence, time series forecasting is crucial in multiple domains.
• Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics
  19 Nov 2018 4 repositories listed
  Natural spatiotemporal processes can be highly non-stationary in many ways, e.
• Temporal Pattern Attention for Multivariate Time Series Forecasting
  12 Sep 2018 4 repositories listed
  To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism.
• Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation
  4 May 2025 3 repositories listed Syntology ran 5 of 7 samples · 2 unverified
  To address this problem, we introduce TimeKD, an efficient MTSF framework that leverages the calibrated language models and privileged knowledge distillation.

Syntology lines on 22 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.