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 826–850 of 6748 papers

TitleStatusHype
Spatial-Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecastingCode1
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic ForecastingCode1
Spatio-Temporal Graph Convolution for Resting-State fMRI AnalysisCode1
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly BenchmarkCode1
Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional StrategiesCode1
Fast Variational Learning in State-Space Gaussian Process ModelsCode1
S-Rocket: Selective Random Convolution Kernels for Time Series ClassificationCode1
Statistical Perspective on Functional and Causal Neural Connectomics: The Time-Aware PC AlgorithmCode1
A Transformer-based Framework for Multivariate Time Series Representation LearningCode1
Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial NetworkCode1
Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative NetworkCode1
Explainable Deep Convolutional Candlestick LearnerCode1
AtsPy: Automated Time Series Forecasting in PythonCode1
Automatic Posterior Transformation for Likelihood-Free InferenceCode1
Expressing Multivariate Time Series as Graphs with Time Series Attention TransformerCode1
Explaining Time Series Predictions with Dynamic MasksCode1
Stop&Hop: Early Classification of Irregular Time SeriesCode1
Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series ClassificationCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Attention based Multi-Modal New Product Sales Time-series ForecastingCode1
Exploring the Advantages of Transformers for High-Frequency TradingCode1
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series dataCode1
Fast, Accurate and Interpretable Time Series Classification Through RandomizationCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
Attention to Warp: Deep Metric Learning for Multivariate 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