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 5176–5200 of 6748 papers

TitleStatusHype
catch22: CAnonical Time-series CHaracteristicsCode1
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian ComputationCode0
Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm—0
Clustering Discrete-Valued Time Series—0
Portfolio Optimization under Fast Mean-reverting and Rough Fractional Stochastic Environment—0
Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics -- A Comprehensive Review—0
Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data—0
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series PredictionCode0
Deep-Learning Inversion of Seismic Data—0
Anomaly detection in the dynamics of web and social networksCode0
Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?—0
Online Estimation of Multiple Dynamic Graphs in Pattern Sequences—0
ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-TermCode0
Spatiotemporal Multi-Graph Convolution Networkfor Ride-hailing Demand ForecastingCode1
Explainable Failure Predictions with RNN Classifiers based on Time Series Data—0
Effective Combination of DenseNet andBiLSTM for Keyword Spotting—0
Predicting Performance using Approximate State Space Model for Liquid State Machines—0
Kernel Change-point Detection with Auxiliary Deep Generative ModelsCode0
Machine learning with neural networksCode0
Efficient Matrix Profile Computation Using Different Distance FunctionsCode0
Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference—0
lassopack: Model selection and prediction with regularized regression in Stata—0
Deep learning-based electroencephalography analysis: a systematic reviewCode0
Predicting Individual Responses to Vasoactive Medications in Children with Septic Shock—0
Synthesising a Database of Parameterised Linear and Non-Linear Invariants for Time-Series Constraints—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