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 3401–3410 of 6748 papers

Title	Date	Tasks	Status
Policy Gradient Reinforcement Learning for Policy Represented by Fuzzy Rules: Application to Simulations of Speed Control of an Automobile	Sep 4, 2020	Time Series Analysis	—Unverified
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification	Dec 13, 2022	AutoMLNeural Architecture Search	—Unverified
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs	May 23, 2019	reinforcement-learningReinforcement Learning	—Unverified
Population size predicts lexical diversity, but so does the mean sea level - why it is important to correctly account for the structure of temporal data	Nov 6, 2015	DiversityTime Series Analysis	—Unverified
Portfolio Optimization under Fast Mean-reverting and Rough Fractional Stochastic Environment	Jan 24, 2019	Portfolio OptimizationTime Series	—Unverified
Portfolio Risk Assessment using Copula Models	Jul 12, 2017	Time SeriesTime Series Analysis	—Unverified
Position-based Content Attention for Time Series Forecasting with Sequence-to-sequence RNNs	Mar 29, 2017	PositionTime Series	—Unverified
Position: Empowering Time Series Reasoning with Multimodal LLMs	Feb 3, 2025	Decision MakingMultimodal Reasoning	—Unverified
Positive blood culture detection in time series data using a BiLSTM network	Dec 3, 2016	Cultural Vocal Bursts Intensity PredictionTime Series	—Unverified
Post-Radiotherapy PET Image Outcome Prediction by Deep Learning under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application	May 22, 2021	Decision MakingDeep 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