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 761–770 of 6748 papers

Title	Date	Tasks	Status	Hype
Multi-Variate Time Series Forecasting on Variable Subsets	Jun 25, 2022	Multivariate Time Series ForecastingTime Series	CodeCode Available	1
Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs	Feb 17, 2022	Multivariate Time Series ForecastingTime Series	CodeCode Available	1
MVMTnet: A Multi-variate Multi-modal Transformer for Multi-class Classification of Cardiac Irregularities Using ECG Waveforms and Clinical Notes	Feb 21, 2023	Binary ClassificationClassification	CodeCode Available	1
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling	Dec 16, 2021	Multivariate Time Series ForecastingPrediction Intervals	CodeCode Available	1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level	May 3, 2022	Intrusion DetectionTime Series	CodeCode Available	1
Building an Automated and Self-Aware Anomaly Detection System	Nov 10, 2020	Anomaly DetectionTime Series	CodeCode Available	1
BolT: Fused Window Transformers for fMRI Time Series Analysis	May 23, 2022	Time SeriesTime Series Analysis	CodeCode Available	1
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors	Sep 9, 2019	Object LocalizationPrediction	CodeCode Available	1
Benchmark time series data sets for PyTorch -- the torchtime package	Jul 25, 2022	Time SeriesTime Series Analysis	CodeCode Available	1
Benchmarking Deep Learning Interpretability in Time Series Predictions	Oct 26, 2020	BenchmarkingDeep Learning	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