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 3276–3300 of 6748 papers

TitleStatusHype
Time Series Counterfactual Inference with Hidden Confounders—0
Symmetry-Augmented Representation for Time Series—0
Latent Space Semi-Supervised Time Series Data Clustering—0
Jumpy Recurrent Neural Networks—0
Anomaly detection in dynamical systems from measured time series—0
An Euler-based GAN for time series—0
Sparsifying Networks via Subdifferential Inclusion—0
Transitional Dynamics of the Saving Rate and Economic Growth—0
How to Identify Investor's types in real financial markets by means of agent based simulation—0
Indirect Measurement of Hepatic Drug Clearance by Fitting Dynamical Models—0
A Multi-modal Deep Learning Model for Video Thumbnail Selection—0
Algorithms for Learning Graphs in Financial MarketsCode0
Ensembles of Localised Models for Time Series ForecastingCode1
A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset—0
Time-Transformed Test for the Explosive Bubbles under Non-stationary Volatility—0
Scalable and Hybrid Ensemble-Based Causality Discovery—0
Memory-Gated Recurrent NetworksCode0
Global Models for Time Series Forecasting: A Simulation StudyCode0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
Machine Learning Advances for Time Series Forecasting—0
Towards Automated Satellite Conjunction Management with Bayesian Deep LearningCode1
Causal Inference from Slowly Varying Nonstationary Processes—0
Time Series Domain Adaptation via Sparse Associative Structure Alignment—0
Generating Long-term Continuous Multi-type Generation Profiles—0
Method for estimating hidden structures determined by unidentifiable state-space models and time-series data based on the Groebner basis—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