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 5501–5525 of 6748 papers

TitleStatusHype
Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal AnalysisCode0
Thresholded ConvNet Ensembles: Neural Networks for Technical Forecasting—0
Process Monitoring Using Maximum Sequence Divergence—0
Learning The Sequential Temporal Information with Recurrent Neural Networks—0
A Variational Time Series Feature Extractor for Action PredictionCode0
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks—0
Mining Illegal Insider Trading of Stocks: A Proactive Approach—0
Dynamic Prediction Length for Time Series with Sequence to Sequence Networks—0
Robust and Scalable Models of Microbiome Dynamics—0
Deep Bayesian Nonparametric Tracking—0
Accurate Uncertainties for Deep Learning Using Calibrated RegressionCode0
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series—0
Stock Movement Prediction from Tweets and Historical PricesCode0
Improving Optimization in Models With Continuous Symmetry Breaking—0
Sampling and Reconstruction of Signals on Product GraphsCode0
An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference—0
Nonlinearity in stock networks—0
Multilevel Wavelet Decomposition Network for Interpretable Time Series AnalysisCode0
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention—0
Complex Gated Recurrent Neural NetworksCode0
What Makes An Asset Useful?—0
A Review of Network Inference Techniques for Neural Activation Time SeriesCode0
Kernel Methods for Nonlinear Connectivity Detection—0
Multi-variable LSTM neural network for autoregressive exogenous model—0
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks—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