Time Series Analysis

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 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.

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

25 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

12 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 1,993 papers with code (6,748 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.

• 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.
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
  4 Mar 2018 35 repositories listed Syntology ran 2 of 10 samples · 8 unverified · 1 pointer-only (licence)
  Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory.
• Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
  7 Oct 2016 25 repositories listed Syntology ran 7 of 14 samples · 7 unverified · 14 pointer-only (licence)
  We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
• 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.
• A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
  7 Apr 2017 13 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has…
• Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline
  20 Nov 2016 12 repositories listed Syntology ran 1 of 10 samples · 9 unverified · 1 pointer-only (licence)
  We propose a simple but strong baseline for time series classification from scratch with deep neural networks.
• Transformers in Time Series: A Survey
  15 Feb 2022 11 repositories listed
  From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis.
• Latent ODEs for Irregularly-Sampled Time Series
  8 Jul 2019 11 repositories listed Syntology ran 4 of 8 samples · 4 unverified · 3 pointer-only (licence)
  Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs).
• Multitask learning and benchmarking with clinical time series data
  22 Mar 2017 11 repositories listed Syntology ran 4 of 40 samples · 36 unverified · 5 pointer-only (licence)
  Health care is one of the most exciting frontiers in data mining and machine learning.
• Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
  19 Nov 2015 11 repositories listed
  One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with…
• 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.
• InceptionTime: Finding AlexNet for Time Series Classification
  11 Sep 2019 10 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  TSC is the area of machine learning tasked with the categorization (or labelling) of time series.
• 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.
• LSTM Fully Convolutional Networks for Time Series Classification
  8 Sep 2017 9 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification.
• Soft-DTW: a Differentiable Loss Function for Time-Series
  5 Mar 2017 9 repositories listed Syntology ran 1 of 11 samples · 10 unverified · 3 pointer-only (licence)
  We propose in this paper a differentiable learning loss between time series, building upon the celebrated dynamic time warping (DTW) discrepancy.
• 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.
• Caulking the Leakage Effect in MEEG Source Connectivity Analysis
  28 Sep 2018 8 repositories listed
  Simplistic estimation of neural connectivity in MEEG sensor space is impossible due to volume conduction.
• Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
  8 Jun 2017 8 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  We also describe novel evaluation methods for GANs, where we generate a synthetic labelled training dataset, and evaluate on a real test set the performance of a model trained on the synthetic data, and vice-versa.
• LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
  1 Jul 2016 8 repositories listed Syntology ran 1 of 9 samples · 8 unverified
  Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine.
• Convolutional Radio Modulation Recognition Networks
  12 Feb 2016 8 repositories listed
  We study the adaptation of convolutional neural networks to the complex temporal radio signal domain.
• Bayesian Online Changepoint Detection
  19 Oct 2007 8 repositories listed Syntology ran 2 of 17 samples · 15 unverified · 3 pointer-only (licence)
  Changepoints are abrupt variations in the generative parameters of a data sequence.
• Discovering physical concepts with neural networks
  26 Jul 2018 7 repositories listed Syntology ran 4 of 6 samples · 2 unverified · 3 pointer-only (licence)
  Despite the success of neural networks at solving concrete physics problems, their use as a general-purpose tool for scientific discovery is still in its infancy.
• Adversarial Robustness Toolbox v1.0.0
  3 Jul 2018 7 repositories listed
  Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging…
• Multivariate LSTM-FCNs for Time Series Classification
  14 Jan 2018 7 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Over the past decade, multivariate time series classification has received great attention.
• 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.

Syntology lines on 24 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.