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 1826–1850 of 6748 papers

TitleStatusHype
On Neural Differential EquationsCode5
Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series—0
Introducing Block-Toeplitz Covariance Matrices to Remaster Linear Discriminant Analysis for Event-related Potential Brain-computer InterfacesCode0
Evaluating conditional covariance estimates via a new targeting approach and a networks-based analysis—0
Decoupling Local and Global Representations of Time SeriesCode1
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series—0
Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite ImageryCode0
Functional Mixtures-of-Experts—0
COVID-19 Hospitalizations Forecasts Using Internet Search Data—0
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT—0
ETSformer: Exponential Smoothing Transformers for Time-series ForecastingCode2
Skeleton-Based Action Segmentation with Multi-Stage Spatial-Temporal Graph Convolutional Neural NetworksCode1
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series ForecastingCode2
Review of automated time series forecasting pipelines—0
Robust Audio Anomaly Detection—0
2T-POT Hawkes model for left- and right-tail conditional quantile forecasts of financial log-returns: out-of-sample comparison of conditional EVT models—0
Methodology for forecasting and optimization in IEEE-CIS 3rd Technical ChallengeCode1
Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecasting—0
A Machine Learning Smartphone-based Sensing for Driver Behavior Classification—0
Weighted Isolation and Random Cut Forest Algorithms for Anomaly Detection—0
Semantic of Cloud Computing services for Time Series workflows—0
Black-box Bayesian inference for economic agent-based models—0
Comparative Study of Machine Learning Models for Stock Price Prediction—0
Partial Sum Processes of Residual-Based and Wald-type Break-Point Statistics in Time Series Regression Models—0
Similarity Learning based Few Shot Learning for ECG Time Series ClassificationCode1
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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