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 6701–6725 of 6748 papers

TitleStatusHype
Transformers predicting the future. Applying attention in next-frame and time series forecastingCode0
Emergence of Functionally Differentiated Structures via Mutual Information Optimization in Recurrent Neural NetworksCode0
VisioRed: A Visualisation Tool for Interpretable Predictive MaintenanceCode0
Causal Discovery with Attention-Based Convolutional Neural NetworksCode0
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantificationCode0
E-LSTM-D: A Deep Learning Framework for Dynamic Network Link PredictionCode0
Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent SpaceCode0
Solving the optimal stopping problem with reinforcement learning: an application in financial option exerciseCode0
Causal Discovery using Model Invariance through Knockoff InterventionsCode0
Multivariate Time Series Classification using Dilated Convolutional Neural NetworkCode0
Rapid training of quantum recurrent neural networksCode0
Multivariate Time Series Classification with WEASEL+MUSECode0
Time Series Prediction by Multi-task GPR with Spatiotemporal Information TransformationCode0
Multivariate Time Series Early Classification Across Channel and Time DimensionsCode0
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
Elastic Product Quantization for Time SeriesCode0
Elastic bands across the path: A new framework and methods to lower bound DTWCode0
Causal discovery for time series with latent confoundersCode0
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous VariablesCode0
Time Series Prediction for Food sustainabilityCode0
GP-VAE: Deep Probabilistic Time Series ImputationCode0
SOM-VAE: Interpretable Discrete Representation Learning on Time SeriesCode0
Efficient Matrix Profile Computation Using Different Distance FunctionsCode0
Real numbers, data science and chaos: How to fit any dataset with a single parameterCode0
Data Augmentation for Generating Synthetic Electrogastrogram Time SeriesCode0
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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