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 5076–5100 of 6748 papers

TitleStatusHype
Predicting Student Performance in an Educational Game Using a Hidden Markov Model—0
Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning—0
Community Detection and Growth Potential Prediction from Patent Citation Networks—0
Identifying cross country skiing techniques using power meters in ski polesCode0
Investigating the impact of autocorrelation on time-varying connectivity—0
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series—0
Random Fragments Classification of Microbial Marker Clades with Multi-class SVM and N-Best Algorithm—0
Transfer Entropy: where Shannon meets Turing—0
One-dimensional Deep Image Prior for Time Series Inverse Problems—0
tsmp: An R Package for Time Series with Matrix Profile—0
Human Motion Prediction via Pattern Completion in Latent Representation Space—0
Explaining Deep Classification of Time-Series Data with Learned PrototypesCode0
Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model—0
DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series prediction—0
Forecasting Weakly Correlated Time Series in Tasks of Electronic Commerce—0
Estimation of Cross-Sectional Dependence in Large Panels—0
Remaining Useful Life Estimation Using Functional Data Analysis—0
Automatic Model Building in GEFCom 2017 Qualifying Match—0
Advanced Customer Activity Prediction based on Deep Hierarchic Encoder-Decoders—0
Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance MatricesCode0
Local Polynomial Estimation of Time-Varying Parameters in Nonlinear Models—0
Fractal Time Series Analysis of Social Network Activities—0
Discovering patterns of online popularity from time seriesCode0
Handling temporality of clinical events with application to Adverse Drug Event detection in Electronic Health Records: A scoping review—0
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective—0
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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