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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 1191–1200 of 6748 papers

Title	Date	Tasks	Status
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals	Mar 10, 2023	Brain Computer InterfaceComputational Efficiency	CodeCode Available
Local-Global Methods for Generalised Solar Irradiance Forecasting	Mar 10, 2023	Solar Irradiance ForecastingTime Series Analysis	—Unverified
On the Soundness of XAI in Prognostics and Health Management (PHM)	Mar 9, 2023	Explainable Artificial Intelligence (XAI)Management	CodeCode Available
Multi-task Meta Label Correction for Time Series Prediction	Mar 9, 2023	Contrastive LearningData Visualization	CodeCode Available
Learning Representation for Anomaly Detection of Vehicle Trajectories	Mar 9, 2023	Anomaly DetectionAutonomous Driving	—Unverified
Efficient Certified Training and Robustness Verification of Neural ODEs	Mar 9, 2023	Time SeriesTime Series Analysis	CodeCode Available
Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals	Mar 9, 2023	Drug Response PredictionEEG	—Unverified
Distributional Vector Autoregression: Eliciting Macro and Financial Dependence	Mar 9, 2023	Time SeriesTime Series Analysis	—Unverified
Self-contained Beta-with-Spikes Approximation for Inference Under a Wright-Fisher Model	Mar 8, 2023	Time SeriesTime Series Analysis	—Unverified
Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test	Mar 8, 2023	BenchmarkingTime Series	CodeCode Available

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All datasets PhysioNet Challenge 2012 Speech Commands Ventilator Pressure Prediction

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	naive classifier	F1	87.47	—	Unverified
2	GRU-D - APC (n = 1)	F1	27.3	—	Unverified
3	GRU-APC (n = 1)	F1	25.7	—	Unverified
4	GRU-D	F1	22.5	—	Unverified
5	GRU	F1	22.3	—	Unverified
6	GRU-Simple	F1	22.2	—	Unverified
7	GRU-Mean	F1	22.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SepTr	% Test Accuracy	98.51	—	Unverified
2	ViT	% Test Accuracy	98.11	—	Unverified
3	FlexTCN-4	% Test Accuracy	97.73	—	Unverified
4	MatchboxNet	% Test Accuracy	97.4	—	Unverified
5	CKCNN (100k)	% Test Accuracy	95.27	—	Unverified
6	FlexTCN-6	% Test Accuracy (Raw Data)	91.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResBiLSTM	MAE	0.13	—	Unverified