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 6001–6025 of 6748 papers

TitleStatusHype
Mosquito Detection with Neural Networks: The Buzz of Deep Learning—0
Machine learning methods for multimedia information retrieval—0
An Investigation of the Structural Characteristics of the Indian IT Sector and the Capital Goods Sector: An Application of the R Programming in Time Series Decomposition and Forecasting—0
Calibration and Filtering of Exponential L\'evy Option Pricing Models—0
ShortFuse: Biomedical Time Series Representations in the Presence of Structured InformationCode0
An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting—0
SILVar: Single Index Latent Variable Models—0
A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes—0
Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors—0
Stochastic modelling of non-stationary financial assets—0
Ensemble Sales Forecasting Study in Semiconductor IndustryCode0
(Quasi)Periodicity Quantification in Video Data, Using TopologyCode0
On Improving Deep Reinforcement Learning for POMDPsCode0
PPMF: A Patient-based Predictive Modeling Framework for Early ICU Mortality Prediction—0
A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector—0
Consistency of Regions of Interest as nodes of functional brain networks measured by fMRICode0
Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models—0
Automatic Anomaly Detection in the Cloud Via Statistical LearningCode0
Time Series Prediction for Graphs in Kernel and Dissimilarity SpacesCode0
A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring—0
Anomaly detection and motif discovery in symbolic representations of time series—0
Non-parametric Estimation of Stochastic Differential Equations with Sparse Gaussian ProcessesCode0
Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks—0
Multivariate Multiscale Dispersion Entropy of Biomedical Times Series—0
Predictive-Corrective Networks for Action Detection—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