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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 6748 papers

TitleStatusHype
ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series DataCode2
HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in PythonCode2
LogAI: A Library for Log Analytics and IntelligenceCode2
NeuralProphet: Explainable Forecasting at ScaleCode2
Generative Time Series Forecasting with Diffusion, Denoise, and DisentanglementCode2
An Extensive Data Processing Pipeline for MIMIC-IVCode2
JANA: Jointly Amortized Neural Approximation of Complex Bayesian ModelsCode2
Flowformer: Linearizing Transformers with Conservation FlowsCode2
Model scale versus domain knowledge in statistical forecasting of chaotic systemsCode2
FITS: Modeling Time Series with 10k ParametersCode2
Liquid Structural State-Space ModelsCode2
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series ForecastingCode2
LSTM-based Encoder-Decoder for Multi-sensor Anomaly DetectionCode2
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based ReconciliationCode2
Minusformer: Improving Time Series Forecasting by Progressively Learning ResidualsCode2
ETSformer: Exponential Smoothing Transformers for Time-series ForecastingCode2
Domino: Discovering Systematic Errors with Cross-Modal EmbeddingsCode2
Multi-Patch Prediction: Adapting LLMs for Time Series Representation LearningCode2
AR-Net: A simple Auto-Regressive Neural Network for time-seriesCode2
Diffusion-based Time Series Imputation and Forecasting with Structured State Space ModelsCode2
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series ForecastingCode2
N-HiTS: Neural Hierarchical Interpolation for Time Series ForecastingCode2
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent NetworksCode2
Deep learning for time series classification: a reviewCode2
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series ForecastingCode2
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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