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 3901–3925 of 6748 papers

TitleStatusHype
Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method—0
Time-series modeling with undecimated fully convolutional neural networks—0
Time series modelling to forecast the confirmed and recovered cases of COVID-19—0
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms—0
Trend-Following Strategies via Dynamic Momentum Learning—0
Time series numerical association rule mining variants in smart agriculture—0
Time Series of Non-Additive Metrics: Identification and Interpretation of Contributing Factors of Variance by Linear Decomposition—0
Time-Series Pattern Recognition in Smart Manufacturing Systems: A Literature Review and Ontology—0
tspDB: Time Series Predict DB—0
Time Series Prediction about Air Quality using LSTM-Based Models: A Systematic Mapping—0
Time Series Prediction : Predicting Stock Price—0
Time Series Prediction using Deep Learning Methods in Healthcare—0
Time series quantile regression using random forests—0
Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation—0
Time Series (re)sampling using Generative Adversarial Networks—0
Time Series Segmentation through Automatic Feature Learning—0
Time Series Simulation by Conditional Generative Adversarial Net—0
Time Series Source Separation with Slow Flows—0
Time Series Structure Discovery via Probabilistic Program Synthesis—0
Time Series Synthesis via Multi-scale Patch-based Generation of Wavelet Scalogram—0
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms—0
Time Series Topic Modeling and Bursty Topic Detection of Correlated News and Twitter—0
Time Series Vector Autoregression Prediction of the Ecological Footprint based on Energy Parameters—0
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations—0
Time-Space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series—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