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 4876–4900 of 6748 papers

TitleStatusHype
Modeling Time-Series and Spatial Data for Recommendations and Other Applications—0
Modeling Time Series Similarity with Siamese Recurrent Networks—0
Modeling Treatment Delays for Patients using Feature Label Pairs in a Time Series—0
Modeling Tweet Arrival Times using Log-Gaussian Cox Processes—0
Modeling Variable Space with Residual Tensor Networks for Multivariate Time Series—0
Modeling Volatility and Dependence of European Carbon and Energy Prices—0
Modeling Website Workload Using Neural Networks—0
Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning—0
Modelling EHR timeseries by restricting feature interaction—0
Modelling Emotion Dynamics in Song Lyrics with State Space Models—0
Modelling matrix time series via a tensor CP-decomposition—0
Modelling Neuronal Behaviour with Time Series Regression: Recurrent Neural Networks on C. Elegans Data—0
Modelling neuronal behaviour with time series regression: Recurrent Neural Networks on synthetic C. elegans data—0
Modelling Reciprocating Relationships with Hawkes Processes—0
Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes—0
Modelling Student Behavior using Granular Large Scale Action Data from a MOOC—0
Modelling the Bitcoin prices and the media attention to Bitcoin via the jump-type processes—0
Model of continuous random cascade processes in financial markets—0
Model Selection in Time Series Analysis: Using Information Criteria as an Alternative to Hypothesis Testing—0
Model-Size Reduction for Reservoir Computing by Concatenating Internal States Through Time—0
Modern strategies for time series regression—0
Modified Auto Regressive Technique for Univariate Time Series Prediction of Solar Irradiance—0
Modified Computation of Correlation Integral for Analyzing Epileptic Signals—0
Modified Profile Likelihood Inference and Interval Forecast of the Burst of Financial Bubbles—0
Modifying memories in a Recurrent Neural Network Unit—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