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 2826–2850 of 6748 papers

TitleStatusHype
All-Clear Flare Prediction Using Interval-based Time Series Classifiers—0
Explaining Outcomes of Multi-Party Dialogues using Causal Learning—0
TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data—0
RotLSTM: Rotating Memories in Recurrent Neural Networks—0
Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noiseCode0
Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation—0
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
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson SamplingCode1
Learning in Feedforward Neural Networks Accelerated by Transfer Entropy—0
Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS—0
Defined the predictors of the lightning over India by using artificial neural network—0
Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods—0
Nonparametric Test for Volatility in Clustered Multiple Time Series—0
Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory (2)—0
Phenotyping OSA: a time series analysis using fuzzy clustering and persistent homology—0
Early Classification of Time Series is Meaningful—0
Initializing LSTM internal states via manifold learning—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
Generative modeling of spatio-temporal weather patterns with extreme event conditioning—0
Stochastic Recurrent Neural Network for Multistep Time Series Forecasting—0
Inductive Predictions of Extreme Hydrologic Events in The Wabash River Watershed—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
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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