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 3701–3725 of 6748 papers

TitleStatusHype
Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets—0
Temporal Network Sampling—0
Temporal Overdrive Recurrent Neural Network—0
Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks—0
Temporal Probabilistic Asymmetric Multi-task Learning—0
Temporal Registration in Application to In-utero MRI Time Series—0
Temporal Registration in In-Utero Volumetric MRI Time Series—0
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction—0
Temporal Sequence Object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series—0
Temporal Spatial Decomposition and Fusion Network for Time Series Forecasting—0
Temporal-Spatial dependencies ENhanced deep learning model (TSEN) for household leverage series forecasting—0
Temporal Subspace Clustering for Human Motion Segmentation—0
Temporal Taylor's scaling of facial electromyography and electrodermal activity in the course of emotional stimulation—0
Temporal Tensor Transformation Network for Multivariate Time Series Prediction—0
Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks—0
Temporal Weights—0
Tensorial Recurrent Neural Networks for Longitudinal Data Analysis—0
Tensor Representation in High-Frequency Financial Data for Price Change Prediction—0
Tensor-Train Networks for Learning Predictive Modeling of Multidimensional Data—0
Testing and Dating Structural Changes in Copula-based Dependence Measures—0
Testing for a Random Walk Structure in the Frequency Evolution of a Tone in Noise—0
Testing for explosive bubbles: a review—0
Testing for Nonlinear Cointegration under Heteroskedasticity—0
Testing Framework for Black-box AI Models—0
Testing of Binary Regime Switching Models using Squeeze Duration Analysis—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