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 2961–2970 of 6748 papers

Title	Date	Tasks	Status
Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware	Oct 10, 2022	Edge-computingimage-classification	—Unverified
Graph-based era segmentation of international financial integration	May 28, 2019	Time SeriesTime Series Analysis	—Unverified
Energy consumption forecasting using a stacked nonparametric Bayesian approach	Nov 11, 2020	Time SeriesTime Series Analysis	—Unverified
Graph-based Predictable Feature Analysis	Feb 1, 2016	Graph EmbeddingTime Series	—Unverified
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks	Oct 27, 2020	Active LearningTime Series	—Unverified
Graph Anomaly Detection in Time Series: A Survey	Jan 31, 2023	Anomaly DetectionGraph Anomaly Detection	—Unverified
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components	Nov 14, 2014	Causal InferenceTime Series	—Unverified
Graph Deep Factors for Forecasting	Oct 14, 2020	Computational EfficiencyTime Series	—Unverified
A Novel Granular-Based Bi-Clustering Method of Deep Mining the Co-Expressed Genes	May 12, 2020	ClusteringTime Series	—Unverified
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning	Jun 28, 2017	Imitation LearningRecommendation Systems	—Unverified

Show:10 25 50

← PrevPage 297 of 675Next →

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