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 1961–1970 of 6748 papers

Title	Date	Tasks	Status	Score
Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case Study	Jul 26, 2019	Deep LearningTime Series Analysis	CodeCode Available	5
Unsupervised Scalable Representation Learning for Multivariate Time Series	Jan 30, 2019	BIG-bench Machine LearningRepresentation Learning	CodeCode Available	5
Discovering physical concepts with neural networks	Jul 26, 2018	Representation Learningscientific discovery	CodeCode Available	5
Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions	Oct 17, 2021	Mortality PredictionTime Series	CodeCode Available	5
Detecting structural perturbations from time series with deep learning	Jun 9, 2020	Bayesian InferenceDeep Learning	CodeCode Available	5
Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series	Mar 14, 2022	Time SeriesTime Series Analysis	CodeCode Available	5
Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm	Jul 7, 2019	GPUTime Series	CodeCode Available	5
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics	Sep 19, 2018	Time SeriesTime Series Analysis	CodeCode Available	5
Beyond Predictions in Neural ODEs: Identification and Interventions	Jun 23, 2021	scientific discoveryTime Series	—Unverified	0
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data	Jun 6, 2022	Representation LearningSelf-Supervised Learning	—Unverified	0

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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