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 2301–2325 of 6748 papers

TitleStatusHype
Automatic deep learning for trend prediction in time series data—0
A network-based transfer learning approach to improve sales forecasting of new products—0
An Estimation of Online Video User Engagement from Features of Continuous Emotions—0
Deep Learning for Time-Series Analysis—0
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies—0
Automatic Construction of a Recurrent Neural Network based Classifier for Vehicle Passage Detection—0
Deep learning for structural health monitoring: An application to heritage structures—0
Deep Learning for Stock Selection Based on High Frequency Price-Volume Data—0
An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition—0
Deep Learning for Satellite Image Time Series Analysis: A Review—0
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data—0
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data—0
Deep learning for ψ-weakly dependent processes—0
Automatic Classification of Irregularly Sampled Time Series with Unequal Lengths: A Case Study on Estimated Glomerular Filtration Rate—0
An Equilibrium Model for the Cross-Section of Liquidity Premia—0
Deep Learning for Plasma Tomography and Disruption Prediction from Bolometer Data—0
Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series—0
Deep Learning for Epidemiological Predictions—0
Automated Testing of AI Models—0
An Ensemble method for Content Selection for Data-to-text Systems—0
A complex network approach to time series analysis with application in diagnosis of neuromuscular disorders—0
Quantile Convolutional Neural Networks for Value at Risk Forecasting—0
Inferring Global Dynamics of a Black-Box System Using Machine Learning—0
Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model—0
Deep Learning for Energy Time-Series Analysis and Forecasting—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