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 1851–1875 of 6748 papers

TitleStatusHype
Deep Q-network using reservoir computing with multi-layered readout—0
DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data—0
Complex systems approach to natural language—0
Real-Time Privacy-Preserving Data Release for Smart Meters—0
A Review on Deep Learning in UAV Remote Sensing—0
Complex market dynamics in the light of random matrix theory—0
Deep Recurrent Electricity Theft Detection in AMI Networks with Random Tuning of Hyper-parameters—0
Deep Recurrent Modelling of Granger Causality with Latent Confounding—0
Complexity Measures and Features for Times Series classification—0
Deep Recurrent Neural Networks for Time Series Prediction—0
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT—0
Deep Reinforcement Learning for Asset Allocation in US Equities—0
A Review of Wind Speed and Wind Power Forecasting Techniques—0
Deep Reinforcement Learning for Portfolio Optimization using Latent Feature State Space (LFSS) Module—0
Deep Reinforcement Learning for Trading—0
Deep Reservoir Networks with Learned Hidden Reservoir Weights using Direct Feedback Alignment—0
A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks—0
Complexity-based Financial Stress Evaluation—0
Deep Sequence Learning for Accurate Gestational Age Estimation from a \$25 Doppler Device—0
Deep Sequence Modeling: Development and Applications in Asset Pricing—0
Complexity and Persistence of Price Time Series of the European Electricity Spot Market—0
A review of two decades of correlations, hierarchies, networks and clustering in financial markets—0
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice—0
Deep State Space Models for Time Series Forecasting—0
Compensatory model for quantile estimation and application to VaR—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