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 3551–3575 of 6748 papers

TitleStatusHype
Time Series Counterfactual Inference with Hidden Confounders—0
CLARE-GAN: GENERATION OF CLASS-SPECIFIC TIME SERIES—0
Sparsifying Networks via Subdifferential Inclusion—0
Symmetry-Augmented Representation for Time Series—0
Algorithms for Learning Graphs in Financial MarketsCode0
A Multi-modal Deep Learning Model for Video Thumbnail Selection—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
Transitional Dynamics of the Saving Rate and Economic Growth—0
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
Machine Learning Advances for Time Series Forecasting—0
Global Models for Time Series Forecasting: A Simulation StudyCode0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
Causal Inference from Slowly Varying Nonstationary Processes—0
Generating Long-term Continuous Multi-type Generation Profiles—0
Time Series Domain Adaptation via Sparse Associative Structure Alignment—0
Method for estimating hidden structures determined by unidentifiable state-space models and time-series data based on the Groebner basis—0
A geometric analysis of nonlinear dynamics and its application to financial time series—0
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior—0
Multi-Faceted Representation Learning with Hybrid Architecture for Time Series Classification—0
COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms—0
Random pattern and frequency generation using a photonic reservoir computer with output feedback—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