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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 1–50 of 6748 papers

TitleStatusHype
Revisiting PCA for time series reduction in temporal dimensionCode7
Foundation Models for Time Series Analysis: A Tutorial and SurveyCode7
TimesNet: Temporal 2D-Variation Modeling for General Time Series AnalysisCode6
On Neural Differential EquationsCode5
TimeMixer++: A General Time Series Pattern Machine for Universal Predictive AnalysisCode5
A Time Series is Worth 64 Words: Long-term Forecasting with TransformersCode5
MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly DetectionCode5
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense MechanismsCode5
Large Language Models for Time Series: A SurveyCode4
Efficient Automated Deep Learning for Time Series ForecastingCode4
Large Models for Time Series and Spatio-Temporal Data: A Survey and OutlookCode4
TimeGPT-1Code4
Stock Price Prediction via Discovering Multi-Frequency Trading PatternsCode4
Transformers in Time Series: A SurveyCode4
Are Transformers Effective for Time Series Forecasting?Code4
Timer: Generative Pre-trained Transformers Are Large Time Series ModelsCode4
"Hey, that's not an ODE": Faster ODE Adjoints via SeminormsCode3
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time SeriesCode3
AER: Auto-Encoder with Regression for Time Series Anomaly DetectionCode3
Time-series Transformer Generative Adversarial NetworksCode3
TSI-Bench: Benchmarking Time Series ImputationCode3
GluonTS: Probabilistic Time Series Models in PythonCode3
Deep Learning for Multivariate Time Series Imputation: A SurveyCode3
Time Series Classification from Scratch with Deep Neural Networks: A Strong BaselineCode3
TOTEM: TOkenized Time Series EMbeddings for General Time Series AnalysisCode3
Datasets: A Community Library for Natural Language ProcessingCode3
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series ForecastingCode3
Greykite: Deploying Flexible Forecasting at Scale at LinkedInCode3
ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and ReasoningCode3
Adversarial Robustness Toolbox v1.0.0Code3
The Rise of Diffusion Models in Time-Series ForecastingCode3
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernelsCode3
Plotly-Resampler: Effective Visual Analytics for Large Time SeriesCode3
Sintel: A Machine Learning Framework to Extract Insights from SignalsCode3
ModernTCN: A Modern Pure Convolution Structure for General Time Series AnalysisCode3
Applications of Deep Neural Networks with KerasCode3
Probabilistic Forecasting with Temporal Convolutional Neural NetworkCode3
LLM4CP: Adapting Large Language Models for Channel PredictionCode3
N-BEATS: Neural basis expansion analysis for interpretable time series forecastingCode3
HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in PythonCode2
How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and OutlookCode2
Harnessing Vision Models for Time Series Analysis: A SurveyCode2
JANA: Jointly Amortized Neural Approximation of Complex Bayesian ModelsCode2
Flowformer: Linearizing Transformers with Conservation FlowsCode2
Generative Time Series Forecasting with Diffusion, Denoise, and DisentanglementCode2
ETSformer: Exponential Smoothing Transformers for Time-series ForecastingCode2
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series ForecastingCode2
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based ReconciliationCode2
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series ForecastingCode2
FITS: Modeling Time Series with 10k ParametersCode2
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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