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 6601–6625 of 6748 papers

TitleStatusHype
Efficient Modeling and Forecasting of the Electricity Spot Price—0
Discriminative Functional Connectivity Measures for Brain Decoding—0
Automatic Construction and Natural-Language Description of Nonparametric Regression ModelsCode0
Human Activity Recognition using Smartphone—0
Kernel Least Mean Square with Adaptive Kernel Size—0
Skill Analysis with Time Series Image Data—0
On change point detection using the fused lasso method—0
Alternating direction method of multipliers for penalized zero-variance discriminant analysis—0
Increasing Server Availability for Overall System Security: A Preventive Maintenance Approach Based on Failure Prediction—0
An Empirical Evaluation of Similarity Measures for Time Series Classification—0
Highly comparative feature-based time-series classification—0
Multi-Step-Ahead Time Series Prediction using Multiple-Output Support Vector Regression—0
Does Restraining End Effect Matter in EMD-Based Modeling Framework for Time Series Prediction? Some Experimental Evidences—0
A Comparative Study of Reservoir Computing for Temporal Signal Processing—0
Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting—0
Distinguishing noise from chaos: objective versus subjective criteria using Horizontal Visibility Graph—0
Fast nonparametric clustering of structured time-series—0
Time series forecasting using neural networks—0
PSO-MISMO Modeling Strategy for Multi-Step-Ahead Time Series Prediction—0
Joint segmentation of multivariate time series with hidden process regression for human activity recognition—0
Time series modeling by a regression approach based on a latent process—0
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression—0
Model-based clustering and segmentation of time series with changes in regime—0
Model-based clustering with Hidden Markov Model regression for time series with regime changes—0
A regression model with a hidden logistic process for feature extraction from time series—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