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 781–790 of 6748 papers

Title	Date	Tasks	Status	Hype	Score
DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data	Feb 17, 2023	Anomaly DetectionDiagnostic	CodeCode Available	1	5
Attention based Multi-Modal New Product Sales Time-series Forecasting	Aug 23, 2020	DecoderNew Product Sales Forecasting	CodeCode Available	1	5
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction	Apr 1, 2020	Time SeriesTime Series Analysis	CodeCode Available	1	5
Dynamical Wasserstein Barycenters for Time-series Modeling	Oct 13, 2021	State EstimationTime Series	CodeCode Available	1	5
Dynamic Data Augmentation with Gating Networks for Time Series Recognition	Nov 5, 2021	Data AugmentationTime Series	CodeCode Available	1	5
Remaining Useful Life Estimation Under Uncertainty with Causal GraphNets	Nov 23, 2020	Time SeriesTime Series Analysis	CodeCode Available	1	5
Dynamic Graph Learning-Neural Network for Multivariate Time Series Modeling	Dec 6, 2021	Graph LearningInductive Bias	CodeCode Available	1	5
Dynamic Sparse Network for Time Series Classification: Learning What to "see''	Dec 19, 2022	Time SeriesTime Series Analysis	CodeCode Available	1	5
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling	Apr 30, 2021	Recommendation SystemsThompson Sampling	CodeCode Available	1	5
From Time Series to Networks in R with the ts2net Package	Aug 20, 2022	Graph MiningTime Series	CodeCode Available	1	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