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 6276–6300 of 6748 papers

TitleStatusHype
Forecasting Emerging Trends from Scientific Literature—0
Optimal Transport vs. Fisher-Rao distance between Copulas for Clustering Multivariate Time Series—0
Unsupervised Representation Learning of Structured Radio Communication SignalsCode0
Temporal Taylor's scaling of facial electromyography and electrodermal activity in the course of emotional stimulation—0
Estimating 3D Trajectories from 2D Projections via Disjunctive Factored Four-Way Conditional Restricted Boltzmann Machines—0
Cognitive state classification using transformed fMRI data—0
Variational inference for rare variant detection in deep, heterogeneous next-generation sequencing data—0
On stabilizing the variance of dynamic functional brain connectivity time series—0
Dynamic process fault prediction using canonical variable trend analysisCode0
Hyperinflation in Brazil, Israel, and Nicaragua revisited—0
Leveraging Network Dynamics for Improved Link Prediction—0
Hierarchical Quickest Change Detection via Surrogates—0
State-space models' dirty little secrets: even simple linear Gaussian models can have estimation problems—0
Analysis of Blink Rate Variability during reading and memory testing—0
GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model—0
On clustering financial time series: a need for distances between dependent random variables—0
Exact Bayesian inference for off-line change-point detection in tree-structured graphical models—0
Optimal trading strategies - a time series approach—0
Clustering Time-Series Energy Data from Smart Meters—0
Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2—0
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis—0
Multi-Scale Convolutional Neural Networks for Time Series ClassificationCode0
Action-Affect Classification and Morphing using Multi-Task Representation Learning—0
Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes—0
The dual frequency RV-coupling coefficient: a novel measure for quantifying cross-frequency information transactions in the brain—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