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 2051–2075 of 6748 papers

TitleStatusHype
Devil in the Detail: Attack Scenarios in Industrial Applications—0
Bayesian prediction of jumps in large panels of time series data—0
An Interpretable and Efficient Infinite-Order Vector Autoregressive Model for High-Dimensional Time Series—0
A First Option Calibration of the GARCH Diffusion Model by a PDE Method—0
A first econometric analysis of the CRIX family—0
A Convolutional Neural Network Approach to Supernova Time-Series Classification—0
Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells—0
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents—0
Bayesian Optimisation for a Biologically Inspired Population Neural Network—0
Development of an Algorithm for Identifying Changes in System Dynamics from Time Series—0
Development and Evaluation of Recurrent Neural Network based Models for Hourly Traffic Volume and AADT Prediction—0
Bayesian Online Change Point Detection for Baseline Shifts—0
An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development—0
Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces—0
A Deterministic Approximation to Neural SDEs—0
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems—0
Affine and Regional Dynamic Time Warpng—0
Deterioration Prediction using Time-Series of Three Vital Signs and Current Clinical Features Amongst COVID-19 Patients—0
Bayesian nonparametric sparse VAR models—0
Detection of small changes in medical and random-dot images comparing self-organizing map performance to human detection—0
Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network—0
Bayesian nonparametric shared multi-sequence time series segmentation—0
The amplitude modulation pattern of Gaussian noise is a fingerprint of Gaussianity—0
Detection of Anomalies in a Time Series Data using InfluxDB and Python—0
Bayesian Nonparametric Adaptive Spectral Density Estimation for Financial Time Series—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