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 2101–2125 of 6748 papers

TitleStatusHype
CATNet: Cross-event Attention-based Time-aware Network for Medical Event Prediction—0
Digital Twin Framework for Time to Failure Forecasting of Wind Turbine Gearbox: A Concept—0
Transformers in Time-series Analysis: A Tutorial—0
Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction—0
COSTI: a New Classifier for Sequences of Temporal IntervalsCode0
Fuzzy Cognitive Maps and Hidden Markov Models: Comparative Analysis of Efficiency within the Confines of the Time Series Classification Task—0
Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version—0
On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification—0
Modeling dynamic volatility under uncertain environment with fuzziness and randomness—0
Topological Signal Processing using the Weighted Ordinal Partition Network—0
Correcting motion induced fluorescence artifacts in two-channel neural imaging—0
Time Series Prediction by Multi-task GPR with Spatiotemporal Information TransformationCode0
Double Diffusion Maps and their Latent Harmonics for Scientific Computations in Latent Space—0
Forecasting foreign exchange rates with regression networks tuned by Bayesian optimization—0
Data-driven prediction and control of extreme events in a chaotic flow—0
GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRU—0
Learning to Attack Powergrids with DERs—0
Quantum Bohmian Inspired Potential to Model Non-Gaussian Events and the Application in Financial Markets—0
Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping—0
Dimension Reduction for time series with Variational AutoEncoders—0
AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphsCode0
On the semantics of big Earth observation data for land classification—0
Multi-sensor Suboptimal Fusion Student's t Filter—0
STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles—0
Time Series Forecasting (TSF) Using Various Deep Learning Models—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