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 6126–6150 of 6748 papers

TitleStatusHype
Probabilistic Broken-Stick Model: A Regression Algorithm for Irregularly Sampled Data with Application to eGFR—0
Autism Spectrum Disorder Classification using Graph Kernels on Multidimensional Time Series—0
Exploring Strategies for Classification of External Stimuli Using Statistical Features of the Plant Electrical Response—0
SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data—0
Split-door criterion: Identification of causal effects through auxiliary outcomesCode0
Times series averaging and denoising from a probabilistic perspective on time-elastic kernelsCode0
Person Re-Identification by Unsupervised Video Matching—0
Multiple Time Series Ising Model for Financial Market Simulations—0
Probabilistic structure discovery in time series data—0
Time Series Structure Discovery via Probabilistic Program Synthesis—0
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets—0
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models—0
Evaluating genetic drift in time-series evolutionary analysis—0
Earliness-Aware Deep Convolutional Networks for Early Time Series Classification—0
Low Latency Anomaly Detection and Bayesian Network Prediction of Anomaly Likelihood—0
Measurement of Anticipative Power of a Retina by Predictive Information—0
Why is it Difficult to Detect Sudden and Unexpected Epidemic Outbreaks in Twitter?—0
Combining observational and experimental data to find heterogeneous treatment effects—0
NonSTOP: A NonSTationary Online Prediction Method for Time Series—0
Learning Time Series Detection Models from Temporally Imprecise Labels—0
Causal Compression—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks—0
The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems—0
All-atom Molecular Dynamics Simulations of the Projection Domain of the Intrinsically Disordered htau40 Protein—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