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 1281–1290 of 6748 papers

Title	Date	Tasks	Status
Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks	Feb 8, 2023	ImputationIrregular Time Series	—Unverified
High-Dimensional Granger Causality for Climatic Attribution	Feb 8, 2023	Time SeriesTime Series Analysis	—Unverified
Short-Term Memory Convolutions	Feb 8, 2023	Acoustic Scene ClassificationScene Classification	—Unverified
Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series	Feb 7, 2023	Anomaly Detectioncounterfactual	—Unverified
CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data	Feb 7, 2023	Causal DiscoveryTime Series	CodeCode Available
Unsupervised Deep Learning for IoT Time Series	Feb 7, 2023	Anomaly DetectionClustering	—Unverified
Penalized Quasi-likelihood Estimation and Model Selection in Time Series Models with Parameters on the Boundary	Feb 6, 2023	Model SelectionTime Series	—Unverified
Tree-Based Learning on Amperometric Time Series Data Demonstrates High Accuracy for Classification	Feb 6, 2023	Feature ImportanceTime Series	—Unverified
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust	Feb 6, 2023	Computational EfficiencyModel Selection	—Unverified
Importance attribution in neural networks by means of persistence landscapes of time series	Feb 6, 2023	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