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 1831–1840 of 6748 papers

Title	Date	Tasks	Status	Score
Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural Network	Nov 19, 2019	Autonomous VehiclesDriver Identification	CodeCode Available	5
Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series Prediction	Jan 2, 2025	GLinearTime Series	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
Domain Adaptation with Representation Learning and Nonlinear Relation for Time Series	Feb 15, 2022	Activity RecognitionDomain Adaptation	CodeCode Available	5
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity Recognition	Jul 10, 2022	Activity RecognitionContrastive Learning	CodeCode Available	5
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal	Dec 7, 2018	EEGElectroencephalogram (EEG)	CodeCode Available	5
Dropout Feature Ranking for Deep Learning Models	Dec 22, 2017	Deep LearningTime Series	CodeCode Available	5
BreizhCrops: A Time Series Dataset for Crop Type Mapping	May 28, 2019	Crop Type MappingTime Series	CodeCode Available	5
Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time Series	Apr 30, 2020	Causal InferencePoint Processes	CodeCode Available	5
BrazilDAM: A Benchmark dataset for Tailings Dam Detection	Mar 17, 2020	Binary ClassificationClassification	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