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 51–100 of 6748 papers

TitleStatusHype
Position: What Can Large Language Models Tell Us about Time Series AnalysisCode2
One Fits All:Power General Time Series Analysis by Pretrained LMCode2
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow PredictionCode2
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly DetectionCode2
N-HiTS: Neural Hierarchical Interpolation for Time Series ForecastingCode2
AST: Audio Spectrogram TransformerCode2
Attention-based CNN-LSTM and XGBoost hybrid model for stock predictionCode2
Satellite Image Time Series Analysis for Big Earth Observation DataCode2
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series ClassificationCode2
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and ProspectsCode2
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series ForecastingCode2
Non-stationary Transformers: Exploring the Stationarity in Time Series ForecastingCode2
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time SeriesCode2
Synthcity: facilitating innovative use cases of synthetic data in different data modalitiesCode2
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing FlowsCode2
Anomaly Transformer: Time Series Anomaly Detection with Association DiscrepancyCode2
MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel MixingCode2
An Extensive Data Processing Pipeline for MIMIC-IVCode2
MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series AnalysisCode2
MOMENT: A Family of Open Time-series Foundation ModelsCode2
Deep Learning for Time Series Forecasting: Tutorial and Literature SurveyCode2
Liquid Structural State-Space ModelsCode2
LibCity: An Open Library for Traffic PredictionCode2
LogAI: A Library for Log Analytics and IntelligenceCode2
How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and OutlookCode2
Harnessing Vision Models for Time Series Analysis: A SurveyCode2
HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in PythonCode2
LSTM-based Encoder-Decoder for Multi-sensor Anomaly DetectionCode2
NeuralProphet: Explainable Forecasting at ScaleCode2
Flowformer: Linearizing Transformers with Conservation FlowsCode2
Generative Time Series Forecasting with Diffusion, Denoise, and DisentanglementCode2
JANA: Jointly Amortized Neural Approximation of Complex Bayesian ModelsCode2
A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic ForecastingCode2
Model scale versus domain knowledge in statistical forecasting of chaotic systemsCode2
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series ForecastingCode2
Liquid Time-constant NetworksCode2
ETSformer: Exponential Smoothing Transformers for Time-series ForecastingCode2
Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and ChallengesCode2
FITS: Modeling Time Series with 10k ParametersCode2
Minusformer: Improving Time Series Forecasting by Progressively Learning ResidualsCode2
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based ReconciliationCode2
Diffusion-based Time Series Imputation and Forecasting with Structured State Space ModelsCode2
Multi-modal Time Series Analysis: A Tutorial and SurveyCode2
Multi-Patch Prediction: Adapting LLMs for Time Series Representation LearningCode2
Domino: Discovering Systematic Errors with Cross-Modal EmbeddingsCode2
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series ForecastingCode2
AR-Net: A simple Auto-Regressive Neural Network for time-seriesCode2
A Survey on Deep Learning based Time Series Analysis with Frequency TransformationCode2
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent NetworksCode2
Deep learning for time series classification: a reviewCode2
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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