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 1611–1620 of 6748 papers

Title	Date	Tasks	Status
Bandwidth-efficient distributed neural network architectures with application to body sensor networks	Oct 14, 2022	ClassificationEEG	—Unverified
Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems	Oct 14, 2022	Anomaly DetectionFault localization	—Unverified
An Empirical Evaluation of Multivariate Time Series Classification with Input Transformation across Different Dimensions	Oct 14, 2022	Data AugmentationTime Series	CodeCode Available
Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing	Oct 14, 2022	Time SeriesTime Series Analysis	—Unverified
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm	Oct 14, 2022	EEGparameter estimation	—Unverified
Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain	Oct 14, 2022	Fault DetectionTime Series	—Unverified
Latent Temporal Flows for Multivariate Analysis of Wearables Data	Oct 14, 2022	Computational EfficiencyTime Series Analysis	—Unverified
Data augmentation on-the-fly and active learning in data stream classification	Oct 13, 2022	Active LearningData Augmentation	CodeCode Available
Marginalized particle Gibbs for multiple state-space models coupled through shared parameters	Oct 13, 2022	Bayesian InferenceState Space Models	—Unverified
Early Discovery of Disappearing Entities in Microblogs	Oct 13, 2022	Time SeriesTime Series Analysis	—Unverified

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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