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 981–990 of 6748 papers

Title	Date	Tasks	Status	Score
Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition	Nov 11, 2022	Activity RecognitionHuman Activity Recognition	CodeCode Available	5
Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution	Feb 6, 2019	CPUTime Series	CodeCode Available	5
Human Activity Recognition using Multi-Head CNN followed by LSTM	Feb 21, 2020	Activity RecognitionBIG-bench Machine Learning	CodeCode Available	5
Hybrid Deep Neural Networks to Infer State Models of Black-Box Systems	Aug 26, 2020	Anomaly DetectionC++ code	CodeCode Available	5
An Information Theory Approach on Deciding Spectroscopic Follow Ups	Nov 6, 2019	General ClassificationTime Series	CodeCode Available	5
Identifying cross country skiing techniques using power meters in ski poles	Apr 23, 2019	BIG-bench Machine LearningTime Series	CodeCode Available	5
Homological Time Series Analysis of Sensor Signals from Power Plants	Jun 3, 2021	Time SeriesTime Series Analysis	CodeCode Available	5
Bayesian Online Changepoint Detection	Oct 19, 2007	Change Point DetectionTime Series	CodeCode Available	5
Highly Scalable and Provably Accurate Classification in Poincare Balls	Sep 8, 2021	ClassificationTime Series Analysis	CodeCode Available	5
Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series Prediction	Mar 6, 2019	Time SeriesTime Series Analysis	CodeCode Available	5

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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