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 1701–1725 of 6748 papers

Title	Date	Tasks	Status	Score
Deep Efficient Continuous Manifold Learning for Time Series Modeling	Dec 3, 2021	Action RecognitionIrregular Time Series	CodeCode Available	5
A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groups	May 31, 2022	Time SeriesTime Series Analysis	CodeCode Available	5
MASA: Motif-Aware State Assignment in Noisy Time Series Data	Sep 6, 2018	ClusteringTime Series	CodeCode Available	5
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series	Dec 19, 2018	Anomaly DetectionTime Series	CodeCode Available	5
Edge computing on TPU for brain implant signal analysis	Feb 28, 2023	Contrastive LearningDeep Learning	CodeCode Available	5
Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time Series	Oct 24, 2016	General ClassificationTime Series	CodeCode Available	5
Early Abandoning PrunedDTW and its application to similarity search	Oct 11, 2020	ClusteringDynamic Time Warping	CodeCode Available	5
Dynamic transformation of prior knowledge into Bayesian models for data streams	Mar 13, 2020	Time SeriesTime Series Analysis	CodeCode Available	5
Capturing the temporal constraints of gradual patterns	Jun 28, 2021	AttributeTime Series Analysis	CodeCode Available	5
Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction	Jan 7, 2018	Missing ValuesTime Series	CodeCode Available	5
Dynamic Virtual Graph Significance Networks for Predicting Influenza	Feb 16, 2021	Representation LearningTime Series	CodeCode Available	5
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes	Oct 22, 2020	Anomaly DetectionTime Series	CodeCode Available	5
Capturing Structure Implicitly from Time-Series having Limited Data	Mar 15, 2018	scientific discoverySmall Data Image Classification	CodeCode Available	5
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series	Dec 5, 2018	Anomaly DetectionTime Series	CodeCode Available	5
Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations	Feb 25, 2022	quantile regressionTime Series	CodeCode Available	5
Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine Learning	Jun 8, 2020	BIG-bench Machine LearningDynamic Time Warping	CodeCode Available	5
Dynamic Time Warping based Adversarial Framework for Time-Series Domain	Jul 9, 2022	Adversarial RobustnessDynamic Time Warping	CodeCode Available	5
EasyMLServe: Easy Deployment of REST Machine Learning Services	Nov 26, 2022	Instance SegmentationSemantic Segmentation	CodeCode Available	5
An Efficient Method for the Classification of Croplands in Scarce-Label Regions	Mar 17, 2021	ClassificationDomain Adaptation	CodeCode Available	5
SAITS: Self-Attention-based Imputation for Time Series	Feb 17, 2022	ImputationMissing Values	CodeCode Available	5
A New Valuation Measure for the Stock Market	May 11, 2019	Time Series Analysis	CodeCode Available	5
Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devices	Oct 31, 2019	DiagnosticGeneral Classification	CodeCode Available	5
Dynamic Natural Language Processing with Recurrence Quantification Analysis	Mar 19, 2018	Time SeriesTime Series Analysis	CodeCode Available	5
Approximate Factor Models for Functional Time Series	Jan 7, 2022	Time SeriesTime Series Analysis	CodeCode Available	5
Dynamic process fault prediction using canonical variable trend analysis	Apr 12, 2016	PredictionTime Series	CodeCode Available	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