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 6651–6675 of 6748 papers

TitleStatusHype
Learning from the past, predicting the statistics for the future, learning an evolving systemCode0
Joint modeling of multiple time series via the beta process with application to motion capture segmentation—0
Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting—0
Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method—0
Nonlinear Time Series Modeling: A Unified Perspective, Algorithm, and Application—0
Word Recognition from Continuous Articulatory Movement Time-series Data using Symbolic Representations—0
A user-centric model of voting intention from Social Media—0
Exploiting Topic based Twitter Sentiment for Stock Prediction—0
Generating Student Feedback from Time-Series Data Using Reinforcement Learning—0
Time-Series Classification Through Histograms of Symbolic Polynomials—0
Multi-horizon solar radiation forecasting for Mediterranean locations using time series models—0
A Direct Estimation of High Dimensional Stationary Vector Autoregressions—0
Gaussian Process Conditional Copulas with Applications to Financial Time Series—0
Deriving land surface phenology indicators from CO2 eddy covariance measurements—0
Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models—0
Frequency-Domain Stochastic Modeling of Stationary Bivariate or Complex-Valued Signals—0
Sparse Auto-Regressive: Robust Estimation of AR Parameters—0
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Emotional Expression Classification using Time-Series Kernels—0
Motif Detection Inspired by Immune Memory (JORS)—0
Structural and Functional Discovery in Dynamic Networks with Non-negative Matrix Factorization—0
Dynamic Covariance Models for Multivariate Financial Time Series—0
Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming—0
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data—0
Identifying Pairs in Simulated Bio-Medical Time-Series—0
Show:102550
← PrevPage 267 of 270Next →
All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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