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 6101–6150 of 6748 papers

TitleStatusHype
Deep multi-survey classification of variable stars—0
Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings—0
Deep Neural Models of Semantic Shift—0
Deep Neural Networks and Neuro-Fuzzy Networks for Intellectual Analysis of Economic Systems—0
Deep Neural Networks for Approximating Stream Reasoning with C-SPARQL—0
Deep Neural Networks for automatic extraction of features in time series satellite images—0
VLSTM: Very Long Short-Term Memory Networks for High-Frequency Trading—0
Deep Neural Networks on EEG signals to predict Attention Score using Gramian Angular Difference Field—0
Deep Neural Networks on EEG Signals to Predict Auditory Attention Score Using Gramian Angular Difference Field—0
Deep Neural Networks to Enable Real-time Multimessenger Astrophysics—0
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series—0
Deep Poisson gamma dynamical systems—0
Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties—0
Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems—0
Deep Q-network using reservoir computing with multi-layered readout—0
DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data—0
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting—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
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
Deep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1—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
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
Deep Sequence Learning for Accurate Gestational Age Estimation from a \$25 Doppler Device—0
Deep Sequence Modeling: Development and Applications in Asset Pricing—0
Deep Signature Statistics for Likelihood-free Time-series Models—0
Deep State Space Models for Time Series Forecasting—0
DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction—0
Deep Subspace Encoders for Nonlinear System Identification—0
Deep Switch Networks for Generating Discrete Data and Language—0
Deep Symbolic Representation Learning for Heterogeneous Time-series Classification—0
Deep Temporal Contrastive Clustering—0
DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series—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
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
Deep Video Prediction for Time Series Forecasting—0
DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions—0
Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection—0
Defined the predictors of the lightning over India by using artificial neural network—0
Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia—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