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 2076–2100 of 6748 papers

TitleStatusHype
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—0
A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization—0
Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings—0
Detecting Structural Breaks in Foreign Exchange Markets by using the group LASSO technique—0
Detecting Slag Formations with Deep Convolutional Neural Networks—0
Bayesian LSTMs in medicine—0
An Incremental Boolean Tensor Factorization approach to model Change Patterns of Objects in Images—0
Detecting Rough Volatility: A Filtering Approach—0
Detecting residues of cosmic events using residual neural network—0
Bayesian inference of natural selection from allele frequency time series—0
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning—0
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization—0
An Improvement of PAA on Trend-Based Approximation for Time Series—0
A Feature Selection Method for Multi-Dimension Time-Series Data—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data—0
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model—0
Detecting Handwritten Mathematical Terms with Sensor Based Data—0
Detecting Gas Vapor Leaks Using Uncalibrated Sensors—0
Bayesian inference and superstatistics to describe long memory processes of financial time series—0
An Improved Online Penalty Parameter Selection Procedure for _1-Penalized Autoregressive with Exogenous Variables—0
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving—0
Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Deep Learning-Based Time Series Forecasting—0
Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signature-based model—0
Detecting Driver's Distraction using Long-term Recurrent Convolutional Network—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