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 381–390 of 6748 papers

Title	Date	Tasks	Status	Hype
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals	Jun 5, 2018	Data AugmentationEEG	CodeCode Available	1
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification	Jan 24, 2021	Brain Computer InterfaceClassification	CodeCode Available	1
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series	Sep 23, 2021	Anomaly DetectionTime Series	CodeCode Available	1
Efficient recurrent architectures through activity sparsity and sparse back-propagation through time	Jun 13, 2022	Gesture RecognitionLanguage Modeling	CodeCode Available	1
Causal Recurrent Variational Autoencoder for Medical Time Series Generation	Jan 16, 2023	Causal InferenceDecoder	CodeCode Available	1
Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network	Apr 26, 2022	DiagnosticTime Series	CodeCode Available	1
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach	Aug 2, 2023	Anomaly DetectionDeep Learning	CodeCode Available	1
Accelerating Recurrent Neural Networks for Gravitational Wave Experiments	Jun 26, 2021	High-Level SynthesisTime Series	CodeCode Available	1
Ensembles of Localised Models for Time Series Forecasting	Dec 30, 2020	ClusteringTime Series	CodeCode Available	1
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values	Feb 21, 2020	Time SeriesTime Series Analysis	CodeCode Available	1

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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