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 291–300 of 6748 papers

Title	Date	Tasks	Status	Hype
Stop&Hop: Early Classification of Irregular Time Series	Aug 21, 2022	Early ClassificationGeneral Classification	CodeCode Available	1
From Time Series to Networks in R with the ts2net Package	Aug 20, 2022	Graph MiningTime Series	CodeCode Available	1
Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data	Aug 19, 2022	Time SeriesTime Series Analysis	CodeCode Available	1
An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly Detection	Aug 19, 2022	Anomaly DetectionRepresentation Learning	CodeCode Available	1
Expressing Multivariate Time Series as Graphs with Time Series Attention Transformer	Aug 19, 2022	Multivariate Time Series ForecastingRepresentation Learning	CodeCode Available	1
Efficient data-driven gap filling of satellite image time series using deep neural networks with partial convolutions	Aug 18, 2022	Image InpaintingTime Series	CodeCode Available	1
Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk	Aug 16, 2022	quantile regressionregression	CodeCode Available	1
Feature-Based Time-Series Analysis in R using the theft Package	Aug 12, 2022	Data VisualizationTime Series	CodeCode Available	1
TSInterpret: A unified framework for time series interpretability	Aug 10, 2022	Interpretable Machine LearningTime Series	CodeCode Available	1
Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting	Aug 5, 2022	Time SeriesTime Series Analysis	CodeCode Available	1

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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