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 431–440 of 6748 papers

Title	Date	Tasks	Status	Hype
Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting	Dec 14, 2021	Graph LearningMultivariate Time Series Forecasting	CodeCode Available	1
Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Series	Dec 14, 2021	Panoptic SegmentationSemantic Segmentation	CodeCode Available	1
Dynamic Graph Learning-Neural Network for Multivariate Time Series Modeling	Dec 6, 2021	Graph LearningInductive Bias	CodeCode Available	1
Parameter Efficient Deep Probabilistic Forecasting	Dec 6, 2021	Probabilistic Time Series ForecastingTime Series	CodeCode Available	1
Anomaly Detection of Wind Turbine Time Series using Variational Recurrent Autoencoders	Dec 5, 2021	Anomaly DetectionTime Series	CodeCode Available	1
ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting	Dec 5, 2021	Load ForecastingTime Series	CodeCode Available	1
Simulation platform for pattern recognition based on reservoir computing with memristor networks	Dec 1, 2021	Time SeriesTime Series Analysis	CodeCode Available	1
Conformal Time-series Forecasting	Dec 1, 2021	Conformal PredictionDecision Making	CodeCode Available	1
Self-supervised Autoregressive Domain Adaptation for Time Series Data	Nov 29, 2021	Domain AdaptationSelf-Supervised Learning	CodeCode Available	1
Improving Deep Learning Interpretability by Saliency Guided Training	Nov 29, 2021	Deep LearningTime Series	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