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 51–75 of 6748 papers

TitleStatusHype
Multi-Patch Prediction: Adapting LLMs for Time Series Representation LearningCode2
MOMENT: A Family of Open Time-series Foundation ModelsCode2
Position: What Can Large Language Models Tell Us about Time Series AnalysisCode2
Minusformer: Improving Time Series Forecasting by Progressively Learning ResidualsCode2
Spatial-Temporal Large Language Model for Traffic PredictionCode2
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly DetectionCode2
FITS: Modeling Time Series with 10k ParametersCode2
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and ProspectsCode2
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time SeriesCode2
Model scale versus domain knowledge in statistical forecasting of chaotic systemsCode2
One Fits All:Power General Time Series Analysis by Pretrained LMCode2
JANA: Jointly Amortized Neural Approximation of Complex Bayesian ModelsCode2
MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel MixingCode2
A Survey on Deep Learning based Time Series Analysis with Frequency TransformationCode2
LogAI: A Library for Log Analytics and IntelligenceCode2
Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular DataCode2
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow PredictionCode2
Synthcity: facilitating innovative use cases of synthetic data in different data modalitiesCode2
ViTs for SITS: Vision Transformers for Satellite Image Time SeriesCode2
Generative Time Series Forecasting with Diffusion, Denoise, and DisentanglementCode2
Towards Long-Term Time-Series Forecasting: Feature, Pattern, and DistributionCode2
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based ReconciliationCode2
SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image UnderstandingCode2
Liquid Structural State-Space ModelsCode2
Diffusion-based Time Series Imputation and Forecasting with Structured State Space ModelsCode2
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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