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 791–800 of 6748 papers

Title	Date	Tasks	Status	Hype	Score
Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting	Sep 4, 2021	Irregular Time SeriesRepresentation Learning	CodeCode Available	1	5
Early Abandoning and Pruning for Elastic Distances including Dynamic Time Warping	Feb 10, 2021	ClusteringDynamic Time Warping	CodeCode Available	1	5
Generalised Interpretable Shapelets for Irregular Time Series	May 28, 2020	Audio ClassificationIrregular Time Series	CodeCode Available	1	5
Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation	Jan 17, 2020	Binary ClassificationChange Point Detection	CodeCode Available	1	5
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	5
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals	Jun 5, 2018	Data AugmentationEEG	CodeCode Available	1	5
A Transformer-based Framework for Multivariate Time Series Representation Learning	Oct 6, 2020	Computational EfficiencyGeneral Classification	CodeCode Available	1	5
General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping	Jul 11, 2019	Dynamic Time WarpingNavigate	CodeCode Available	1	5
FusionLane: Multi-Sensor Fusion for Lane Marking Semantic Segmentation Using Deep Neural Networks	Mar 9, 2020	PositionSegmentation	CodeCode Available	1	5
Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty Quantification	May 28, 2021	EpidemiologyTime 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