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 511–520 of 6748 papers

Title	Date	Tasks	Status	Hype	Score
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction	Aug 20, 2021	Deep LearningTime Series	CodeCode Available	1	5
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels	Jul 12, 2022	Action DetectionActivity Detection	CodeCode Available	1	5
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift	Apr 7, 2022	Domain AdaptationTime Series	CodeCode Available	1	5
Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series Transformer	Jul 6, 2022	DenoisingTime Series	CodeCode Available	1	5
ARMA Cell: A Modular and Effective Approach for Neural Autoregressive Modeling	Aug 31, 2022	Time SeriesTime Series Analysis	CodeCode Available	1	5
AGNet: Weighing Black Holes with Machine Learning	Nov 30, 2020	BIG-bench Machine LearningTime Series	CodeCode Available	1	5
AGNet: Weighing Black Holes with Deep Learning	Aug 17, 2021	Deep LearningTime Series	CodeCode Available	1	5
Are we certain it's anomalous?	Nov 16, 2022	Anomaly DetectionTime Series	CodeCode Available	1	5
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales	Aug 26, 2021	Missing ValuesTime Series Analysis	CodeCode Available	1	5
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models	Oct 7, 2016	DiversityImage Captioning	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