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 1691–1700 of 6748 papers

Title	Date	Tasks	Status	Score
Early Abandoning PrunedDTW and its application to similarity search	Oct 11, 2020	ClusteringDynamic Time Warping	CodeCode Available	5
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes	Oct 22, 2020	Anomaly DetectionTime Series	CodeCode Available	5
An attention model to analyse the risk of agitation and urinary tract infections in people with dementia	Jan 18, 2021	Data IntegrationManagement	CodeCode Available	5
Edge computing on TPU for brain implant signal analysis	Feb 28, 2023	Contrastive LearningDeep Learning	CodeCode Available	5
Dynamic transformation of prior knowledge into Bayesian models for data streams	Mar 13, 2020	Time SeriesTime Series Analysis	CodeCode Available	5
Dynamic Virtual Graph Significance Networks for Predicting Influenza	Feb 16, 2021	Representation LearningTime Series	CodeCode Available	5
A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groups	May 31, 2022	Time SeriesTime Series Analysis	CodeCode Available	5
MASA: Motif-Aware State Assignment in Noisy Time Series Data	Sep 6, 2018	ClusteringTime Series	CodeCode Available	5
Reinforcement Learning for Portfolio Management	Sep 12, 2019	Data AugmentationManagement	CodeCode Available	5
Dynamic Time Warping based Adversarial Framework for Time-Series Domain	Jul 9, 2022	Adversarial RobustnessDynamic Time Warping	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