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 3676–3700 of 6748 papers

TitleStatusHype
Teaching DNNs to design fast fashion—0
Teaching Recurrent Neural Networks to Modify Chaotic Memories by Example—0
Technical Progress Analysis Using a Dynamic Topic Model for Technical Terms to Revise Patent Classification Codes—0
Techniques for clustering interaction data as a collection of graphs—0
Techniques for multifractal spectrum estimation in financial time series—0
Techniques for visualizing LSTMs applied to electrocardiograms—0
TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data—0
TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services—0
Telling cause from effect in deterministic linear dynamical systems—0
Tempered Stable Processes with Time Varying Exponential Tails—0
TempNet: Online Semantic Segmentation on Large-Scale Point Cloud Series—0
Temporal Autoencoding Improves Generative Models of Time Series—0
Temporal-Clustering Invariance in Irregular Healthcare Time Series—0
Temporal Clustering of Time Series via Threshold Autoregressive Models: Application to Commodity Prices—0
Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting—0
Temporal Disaggregation of the Cumulative Grass Growth—0
Temporal Embedding in Convolutional Neural Networks for Robust Learning of Abstract Snippets—0
Temporal Feature Selection on Networked Time Series—0
Temporal-Framing Adaptive Network for Heart Sound Segmentation without Prior Knowledge of State Duration—0
Temporal Graph Convolutional Networks for Automatic Seizure Detection—0
Temporal Graph Signal Decomposition—0
OptStream: Releasing Time Series Privately—0
Temporal-lobe Epilepsy: Harmonic and Anharmonic Periodicity in Microeletrode Voltage—0
Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data—0
Temporally Folded Convolutional Neural Networks for Sequence Forecasting—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