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 1601–1610 of 6748 papers

Title	Date	Tasks	Status	Score
Online Change Point Detection for Weighted and Directed Random Dot Product Graphs	Jan 26, 2022	Change Point DetectionGraph Representation Learning	CodeCode Available	5
EnK: Encoding time-information in convolution	Jun 7, 2020	EEGElectroencephalogram (EEG)	CodeCode Available	5
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational Costs	Feb 22, 2017	ClassificationDiversity	CodeCode Available	5
Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-Series	Nov 3, 2024	Data IntegrationFeature Engineering	CodeCode Available	5
Probabilistic Traffic Forecasting with Dynamic Regression	Jan 17, 2023	regressionTime Series	CodeCode Available	5
On Regularizability and its Application to Online Control of Unstable LTI Systems	May 29, 2020	Time Series Analysis	CodeCode Available	5
Enhancing Identification of Structure Function of Academic Articles Using Contextual Information	Nov 28, 2021	ArticlesDeep Learning	CodeCode Available	5
On Mini-Batch Training with Varying Length Time Series	Dec 13, 2022	Dynamic Time WarpingTime Series	CodeCode Available	5
End-to-end learning of energy-based representations for irregularly-sampled signals and images	Oct 1, 2019	Earth ObservationTime Series	CodeCode Available	5
End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping	Jan 30, 2019	ClassificationCrop Classification	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