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 6251–6275 of 6748 papers

TitleStatusHype
Kolmogorov Space in Time Series Data—0
Specific Differential Entropy Rate Estimation for Continuous-Valued Time SeriesCode0
Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach—0
shapeDTW: shape Dynamic Time WarpingCode0
Forecasting Framework for Open Access Time Series in Energy—0
Solving Temporal Puzzles—0
Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks—0
Deep Canonical Time Warping—0
ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering—0
Variational Bayesian Inference for Hidden Markov Models With Multivariate Gaussian Output Distributions—0
Foreign exchange risk premia: from traditional to state-space analyses—0
Linear Credit Risk Models—0
Initial conditions in the neural field model—0
Nonlinear trend removal should be carefully performed in heart rate variability analysis—0
Gaussian variational approximation with sparse precision matrices—0
Automatic Classification of Irregularly Sampled Time Series with Unequal Lengths: A Case Study on Estimated Glomerular Filtration Rate—0
Learning zero-cost portfolio selection with pattern matching—0
With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams—0
Direct Method for Training Feed-forward Neural Networks using Batch Extended Kalman Filter for Multi-Step-Ahead Predictions—0
Inference of High-dimensional Autoregressive Generalized Linear Models—0
Clustering Time Series and the Surprising Robustness of HMMs—0
Brain Emotional Learning-Based Prediction Model (For Long-Term Chaotic Prediction Applications)—0
ODE - Augmented Training Improves Anomaly Detection in Sensor Data from Machines—0
Temporal Clustering of Time Series via Threshold Autoregressive Models: Application to Commodity Prices—0
Analyzing Time Series Changes of Correlation between Market Share and Concerns on Companies measured through Search Engine Suggests—0
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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