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 451–475 of 6748 papers

TitleStatusHype
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulationsCode1
Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic RepresentationsCode1
Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic BuildingsCode1
Evaluation of post-hoc interpretability methods in time-series classificationCode1
catch22: CAnonical Time-series CHaracteristicsCode1
A Neural PDE Solver with Temporal Stencil ModelingCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time SeriesCode1
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series DataCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
LEAD1.0: A Large-scale Annotated Dataset for Energy Anomaly Detection in Commercial BuildingsCode1
Learning Differential Equations that are Easy to SolveCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
Learning Fast and Slow for Online Time Series ForecastingCode1
Adversarial Sparse Transformer for Time Series ForecastingCode1
Learning Graph Neural Networks for Multivariate Time Series Anomaly DetectionCode1
A Comprehensive Survey of Regression Based Loss Functions for Time Series ForecastingCode1
Learning Linear Dynamical Systems via Spectral FilteringCode1
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series ForecastingCode1
Learning the dynamics of technical trading strategiesCode1
TS2Vec: Towards Universal Representation of Time SeriesCode1
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse ObservationsCode1
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuesCode1
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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