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 701–725 of 6748 papers

TitleStatusHype
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of ProgressCode1
Instance-based Counterfactual Explanations for Time Series ClassificationCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning ModelsCode1
Time-series Imputation and Prediction with Bi-Directional Generative Adversarial NetworksCode1
Neural Rough Differential Equations for Long Time SeriesCode1
Time series forecasting with Gaussian Processes needs priorsCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
XCM: An Explainable Convolutional Neural Network for Multivariate Time Series ClassificationCode1
Deep Switching Auto-Regressive Factorization:Application to Time Series ForecastingCode1
tsBNgen: A Python Library to Generate Time Series Data from an Arbitrary Dynamic Bayesian Network StructureCode1
Multivariate Time-series Anomaly Detection via Graph Attention NetworkCode1
Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action RecognitionCode1
Counterfactual Explanations for Machine Learning on Multivariate Time Series DataCode1
USAD: UnSupervised Anomaly Detection on Multivariate Time SeriesCode1
Attention based Multi-Modal New Product Sales Time-series ForecastingCode1
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant RepresentationCode1
TAnoGAN: Time Series Anomaly Detection with Generative Adversarial NetworksCode1
Learning from Irregularly-Sampled Time Series: A Missing Data PerspectiveCode1
HiPPO: Recurrent Memory with Optimal Polynomial ProjectionsCode1
Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and ApplicationCode1
Principles and Algorithms for Forecasting Groups of Time Series: Locality and GlobalityCode1
Score-informed Networks for Music Performance AssessmentCode1
An Empirical Survey of Data Augmentation for Time Series Classification with Neural NetworksCode1
FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic ForecastingCode1
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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