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 1211–1220 of 6748 papers

Title	Date	Tasks	Status
Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent Space	Mar 3, 2023	Self-Supervised LearningSensitivity	CodeCode Available
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections	Mar 3, 2023	Time SeriesTime Series Analysis	CodeCode Available
Reservoir computing based on solitary-like waves dynamics of film flows: a proof of concept	Mar 3, 2023	Time SeriesTime Series Analysis	—Unverified
Spectral learning of Bernoulli linear dynamical systems models	Mar 3, 2023	Decision MakingTime Series	CodeCode Available
Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming	Mar 3, 2023	Time SeriesTime Series Analysis	—Unverified
Building Floorspace in China: A Dataset and Learning Pipeline	Mar 3, 2023	Time Series Analysis	—Unverified
Multi-Task Self-Supervised Time-Series Representation Learning	Mar 2, 2023	Anomaly DetectionContrastive Learning	—Unverified
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series	Mar 2, 2023	Anomaly DetectionTime Series	CodeCode Available
Interpretable System Identification and Long-term Prediction on Time-Series Data	Mar 2, 2023	PredictionTime Series	—Unverified
RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series	Mar 1, 2023	Anomaly DetectionTime Series	—Unverified

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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