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 1711–1720 of 6748 papers

Title	Date	Tasks	Status
DynaConF: Dynamic Forecasting of Non-Stationary Time Series	Sep 17, 2022	Deep LearningTime Series	CodeCode Available
DBT-DMAE: An Effective Multivariate Time Series Pre-Train Model under Missing Data	Sep 16, 2022	Time SeriesTime Series Analysis	—Unverified
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series	Sep 16, 2022	Model SelectionSuper-Resolution	CodeCode Available
Information Theoretic Measures of Causal Influences during Transient Neural Events	Sep 15, 2022	Time SeriesTime Series Analysis	—Unverified
Out-of-Distribution Representation Learning for Time Series Classification	Sep 15, 2022	Activity RecognitionClassification	—Unverified
Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description	Sep 15, 2022	Interpretable Machine LearningTime Series	—Unverified
Efficient learning of nonlinear prediction models with time-series privileged information	Sep 15, 2022	PredictionRepresentation Learning	CodeCode Available
Universal abundance fluctuations across microbial communities, tropical forests, and urban populations	Sep 15, 2022	Time SeriesTime Series Analysis	—Unverified
Statistical Properties of the Entropy from Ordinal Patterns	Sep 15, 2022	Time SeriesTime Series Analysis	—Unverified
FRANS: Automatic Feature Extraction for Time Series Forecasting	Sep 15, 2022	Dimensionality ReductionMeta-Learning	—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