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 6526–6550 of 6748 papers

TitleStatusHype
Reasoning for Improved Sensor Data Interpretation in a Smart Home—0
Generative Modeling of Hidden Functional Brain Networks—0
Reverse Engineering Chemical Reaction Networks from Time Series Data—0
Pooled Motion Features for First-Person VideosCode0
High Dimensional Forecasting via Interpretable Vector Autoregression—0
ANN Model to Predict Stock Prices at Stock Exchange Markets—0
Estimation of Large Covariance and Precision Matrices from Temporally Dependent ObservationsCode0
Machine Learning for Neuroimaging with Scikit-LearnCode0
Subspace based low rank and joint sparse matrix recovery—0
Highly comparative fetal heart rate analysis—0
Coresets for k-Segmentation of Streaming Data—0
Covariance shrinkage for autocorrelated data—0
On-line Summarization of Time-series Documents using a Graph-based Algorithm—0
Multi-scale Graphical Models for Spatio-Temporal Processes—0
How to monitor and mitigate stair-casing in l1 trend filtering—0
Learning convolution filters for inverse covariance estimation of neural network connectivity—0
An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in RCode0
Tree-structured Gaussian Process Approximations—0
Modeling Deep Temporal Dependencies with Recurrent Grammar Cells""—0
Analysis of Brain States from Multi-Region LFP Time-Series—0
Clustering evolving data using kernel-based methods—0
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components—0
Statistically Significant Detection of Linguistic Change—0
A Short Image Series Based Scheme for Time Series Digital Image Correlation—0
Fast Function to Function Regression—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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