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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 1851–1900 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 market dynamics in the light of random matrix theory—0
Real-Time Privacy-Preserving Data Release for Smart Meters—0
Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach—0
Complexity Measures and Features for Times Series classification—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
A Review of Wind Speed and Wind Power Forecasting Techniques—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 Method for Estimating the Entropy of Time Series Using Artificial Neural Networks—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
Complexity-based Financial Stress Evaluation—0
Complexity and Persistence of Price Time Series of the European Electricity Spot Market—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
A review of two decades of correlations, hierarchies, networks and clustering in financial markets—0
Deep Signature Statistics for Likelihood-free Time-series Models—0
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice—0
Deep State Space Models for Time Series Forecasting—0
DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction—0
Compensatory model for quantile estimation and application to VaR—0
Deep Subspace Encoders for Nonlinear System Identification—0
Accurate Prediction of Global Mean Temperature through Data Transformation Techniques—0
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets—0
Discovering Hidden Physics Behind Transport Dynamics—0
Deep Symbolic Representation Learning for Heterogeneous Time-series Classification—0
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors—0
Deep Temporal Contrastive Clustering—0
A New State-of-the-Art Transformers-Based Load Forecaster on the Smart Grid Domain—0
A Video Recognition Method by using Adaptive Structural Learning of Long Short Term Memory based Deep Belief Network—0
DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series—0
A review of predictive uncertainty estimation with machine learning—0
A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data—0
Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression—0
Deep Time Series Models for Scarce Data—0
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO—0
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals—0
Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases—0
Deep Transformer Model with Pre-Layer Normalization for COVID-19 Growth Prediction—0
Deep Transformer Networks for Time Series Classification: The NPP Safety Case—0
DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction—0
Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives—0
A Review of Open Source Software Tools for Time Series Analysis—0
Discovering Causal Relations in Textual Instructions—0
Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling—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