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 1076–1100 of 6748 papers

TitleStatusHype
Classifying Human Activities using Machine Learning and Deep Learning Techniques—0
Automatic time-series phenotyping using massive feature extraction—0
Automatic Synthesis of Neurons for Recurrent Neural Nets—0
Automatic Stockpile Volume Monitoring using Multi-view Stereo from SkySat Imagery—0
An Evaluation of Classification and Outlier Detection Algorithms—0
Classifiers With a Reject Option for Early Time-Series Classification—0
Classifying Image Sequences of Astronomical Transients with Deep Neural Networks—0
Automatic Response Assessment in Regions of Language Cortex in Epilepsy Patients Using ECoG-based Functional Mapping and Machine Learning—0
Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone—0
Automatic Registration and Clustering of Time Series—0
A Neural Stochastic Volatility Model—0
Automatic Generation of Student Report Cards—0
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces—0
Automatic Generation of Probabilistic Programming from Time Series Data—0
Automatic Forecasting via Meta-Learning—0
A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices—0
A Composite Quantile Fourier Neural Network for Multi-Step Probabilistic Forecasting of Nonstationary Univariate Time Series—0
Classification with the matrix-variate-t distribution—0
Classifying Pattern and Feature Properties to Get a Θ(n) Checker and Reformulation for Sliding Time-Series Constraints—0
Automatic estimation of heading date of paddy rice using deep learning—0
A Neural Implementation of the Kalman Filter—0
Automatic Error Analysis of Human Motor Performance for Interactive Coaching in Virtual Reality—0
An Euler-based GAN for time series—0
A complex network approach to time series analysis with application in diagnosis of neuromuscular disorders—0
Automatic Detection Of Noise Events at Shooting Range Using Machine Learning—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