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 3176–3200 of 6748 papers

TitleStatusHype
Predicting Intraoperative Hypoxemia with Hybrid Inference Sequence Autoencoder Networks—0
PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback—0
Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS—0
Learning in Feedforward Neural Networks Accelerated by Transfer Entropy—0
Defined the predictors of the lightning over India by using artificial neural network—0
Nonparametric Test for Volatility in Clustered Multiple Time Series—0
Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods—0
Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory (2)—0
Initializing LSTM internal states via manifold learning—0
Early Classification of Time Series is Meaningful—0
Phenotyping OSA: a time series analysis using fuzzy clustering and persistent homology—0
Generative modeling of spatio-temporal weather patterns with extreme event conditioning—0
Stochastic Recurrent Neural Network for Multistep Time Series Forecasting—0
Bridging observation, theory and numerical simulation of the ocean using Machine Learning—0
tsrobprep - an R package for robust preprocessing of time series data—0
System identification using Bayesian neural networks with nonparametric noise models—0
Accuracy Improvement for Fully Convolutional Networks via Selective Augmentation with Applications to Electrocardiogram Data—0
Inductive Predictions of Extreme Hydrologic Events in The Wabash River Watershed—0
Deep Convolutional Neural Network for Non-rigid Image Registration—0
Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect—0
Learning Hamiltonian dynamics by reservoir computer—0
Neural circuits for dynamics-based segmentation of time seriesCode0
Predicting the Number of Reported Bugs in a Software RepositoryCode0
Normalized multivariate time series causality analysis and causal graph reconstruction—0
Sequential convolutional network for behavioral pattern extraction in gait recognition—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