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 901–910 of 6748 papers

Title	Date	Tasks	Status	Hype
TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning	May 29, 2025	Dynamic Time WarpingKeypoint Detection	CodeCode Available	1
Neural Transformation Learning for Deep Anomaly Detection Beyond Images	Mar 30, 2021	Anomaly DetectionSelf-Supervised Learning	CodeCode Available	1
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations	Apr 13, 2021	Time SeriesTime Series Analysis	CodeCode Available	1
Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning	Sep 14, 2021	counterfactualCounterfactual Reasoning	—Unverified	0
Anomalous volatility scaling in high frequency financial data	Dec 16, 2015	Time SeriesTime Series Analysis	—Unverified	0
A Framework for Exploring Non-Linear Functional Connectivity and Causality in the Human Brain: Mutual Connectivity Analysis (MCA) of Resting-State Functional MRI with Convergent Cross-Mapping and Non-Metric Clustering	Jul 14, 2014	ClusteringFunctional Connectivity	—Unverified	0
ANN Model to Predict Stock Prices at Stock Exchange Markets	Dec 17, 2014	PredictionTime Series	—Unverified	0
An NLP-Assisted Bayesian Time Series Analysis for Prevalence of Twitter Cyberbullying During the COVID-19 Pandemic	Jul 23, 2022	Time SeriesTime Series Analysis	—Unverified	0
A Framework for Evaluating the Impact of Food Security Scenarios	Jan 23, 2023	Decision MakingTime Series	—Unverified	0
Actionable Interpretation of Machine Learning Models for Sequential Data: Dementia-related Agitation Use Case	Sep 10, 2020	BIG-bench Machine LearningTime Series	—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