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 5751–5775 of 6748 papers

TitleStatusHype
Estimation and Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels under Weak Dependence—0
A Composite Quantile Fourier Neural Network for Multi-Step Probabilistic Forecasting of Nonstationary Univariate Time Series—0
An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework—0
Co-Morbidity Exploration on Wearables Activity Data Using Unsupervised Pre-training and Multi-Task Learning—0
Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series—0
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
Online Forecasting Matrix Factorization—0
Towards dense object tracking in a 2D honeybee hiveCode0
Dropout Feature Ranking for Deep Learning ModelsCode0
DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity—0
Multi-task learning of time series and its application to the travel demand—0
Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data—0
Multi-dimensional Graph Fourier Transform—0
Estimating activity cycles with probabilistic methods I. Bayesian Generalised Lomb-Scargle Periodogram with TrendCode0
Lost in Time: Temporal Analytics for Long-Term Video Surveillance—0
Contemporary machine learning: a guide for practitioners in the physical sciences—0
Multi-shot Pedestrian Re-identification via Sequential Decision Making—0
A Shapelet Transform for Multivariate Time Series Classification—0
Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things—0
Accurate Inference for Adaptive Linear ModelsCode0
Dynamic Weight Alignment for Temporal Convolutional Neural Networks—0
Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions—0
Experimental design trade-offs for gene regulatory network inference: an in silico study of the yeast Saccharomyces cerevisiae cell cycle—0
Spatial-temporal wind field prediction by Artificial Neural Networks—0
Causal Patterns: Extraction of multiple causal relationships by Mixture of Probabilistic Partial Canonical Correlation AnalysisCode0
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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