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 5576–5600 of 6748 papers

TitleStatusHype
High Dimensional Time Series Generators—0
On Improving Deep Reinforcement Learning for POMDPs—0
Option Pricing under Fast-varying and Rough Stochastic Volatility—0
Detecting Concrete Abnormality Using Time-series Thermal Imaging and Supervised Learning—0
An interpretable LSTM neural network for autoregressive exogenous model—0
Predicting Cyber Events by Leveraging Hacker Sentiment—0
Causal Inference via Kernel Deviance Measures—0
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEsCode0
Model identification for ARMA time series through convolutional neural networks—0
Anomaly Detection for Industrial Big Data—0
Expressway visibility estimation based on image entropy and piecewise stationary time series analysis—0
Direct Estimation of Pharmacokinetic Parameters from DCE-MRI using Deep CNN with Forward Physical Model Loss—0
Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Space—0
Real-time Air Pollution prediction model based on Spatiotemporal Big dataCode1
micompm: A MATLAB/Octave toolbox for multivariate independent comparison of observationsCode0
Novel Fourier Quadrature Transforms and Analytic Signal Representations for Nonlinear and Non-stationary Time Series Analysis—0
Spatial heterogeneity analyses identify limitations of epidemic alert systems: Monitoring influenza-like illness in France—0
Bag of Recurrence Patterns Representation for Time-Series Classification—0
Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the YearsCode0
Epileptic Seizure Detection: A Deep Learning Approach—0
Kinetic Compressive Sensing—0
Non-Linear Temporal Subspace Representations for Activity Recognition—0
Inferring network connectivity from event timing patternsCode0
MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series ForecastingCode0
Scalable photonic reinforcement learning by time-division multiplexing of laser chaos—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