SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

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Showing 876–900 of 6748 papers

TitleStatusHype
An Empirical Evaluation of Time-Series Feature SetsCode1
Advancing the State-of-the-Art for ECG Analysis through Structured State Space ModelsCode1
Time Series Embedding Methods for Classification Tasks: A ReviewCode1
An Empirical Framework for Domain Generalization in Clinical SettingsCode1
Forecasting Sequential Data using Consistent Koopman AutoencodersCode1
Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series ClassificationCode1
Hierarchical forecasting with a top-down alignment of independent level forecastsCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
Time Series Representation ModelsCode1
Forecasting with Deep LearningCode1
Backdoor Attacks on Time Series: A Generative ApproachCode1
Improved Online Conformal Prediction via Strongly Adaptive Online LearningCode1
Forecasting with time series imagingCode1
Informer: Beyond Efficient Transformer for Long Sequence Time-Series ForecastingCode1
ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series ForecastingCode1
Evaluation of post-hoc interpretability methods in time-series classificationCode1
FrAug: Frequency Domain Augmentation for Time Series ForecastingCode1
From Fourier to Koopman: Spectral Methods for Long-term Time Series PredictionCode1
From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecastingCode1
Learning future terrorist targets through temporal meta-graphsCode1
Neural Rough Differential Equations for Long Time SeriesCode1
A Bayesian neural network predicts the dissolution of compact planetary systemsCode1
Generative adversarial networks in time series: A survey and taxonomyCode1
Self-Supervised Learning for Time Series: Contrastive or Generative?Code1
WOODS: Benchmarks for Out-of-Distribution Generalization in Time SeriesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified