Results 11 to 20 of about 411,481 (262)
Temperature Time Series Prediction Model Based on Time Series Decomposition and Bi-LSTM Network
Mathematics, 2023 Utilizing a temperature time-series prediction model to achieve good results can help us to accurately sense the changes occurring in temperature levels in advance, which is important for human life.
Kun Zhang, Xing Huo, Kun Shao
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Review of Deep Learning Applied to Time Series Prediction [PDF]
Jisuanji kexue yu tansuo, 2023 The time series is generally a set of random variables that are observed and collected at a certain frequency in the course of something??s development.
LIANG Hongtao, LIU Shuo, DU Junwei, HU Qiang, YU Xu
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Time Series Prediction Based on LSTM-Attention-LSTM Model
IEEE Access, 2023 Time series forecasting uses data from the past periods of time to predict future information, which is of great significance in many applications. Existing time series forecasting methods still have problems such as low accuracy when dealing with some ...
Xianyun Wen, Weibang Li
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COVID-19 Time Series Prediction
Procedia Computer Science, 2021 Abstract The Artificial Neural Network (ANN) is a computer technique that uses a mathematical model to represent a simpler form of the biologic neural structure. It is formed by many processing units and its intelligent behavior comes from the iterations between these units.
Oliveira, Leonardo Sestrem de +2 more
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Predicting chaotic time series [PDF]
Physical Review Letters, 1987 We present a forecasting technique for chaotic data. After embedding a time series in a state space using delay coordinates, we ``learn'' the induced nonlinear mapping using local approximation. This allows us to make short-term predictions of the future behavior of a time series, using information based only on past values.
Farmer, J. Doyne, Sidorowich, John J.
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Self-Attentive Moving Average for Time Series Prediction
Applied Sciences, 2022 Time series prediction has been studied for decades due to its potential in a wide range of applications. As one of the most popular technical indicators, moving average summarizes the overall changing patterns over a past period and is frequently used ...
Yaxi Su, Chaoran Cui, Hao Qu
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Polynomial Fuzzy Information Granule-Based Time Series Prediction
Mathematics, 2022 Fuzzy information granulation transfers the time series analysis from the numerical platform to the granular platform, which enables us to study the time series at a different granularity. In previous studies, each fuzzy information granule in a granular
Xiyang Yang +3 more
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Poster: Visual prediction of time series [PDF]
2009 IEEE Symposium on Visual Analytics Science and Technology, 2009 Many well-known time series prediction methods have been used daily by analysts making decisions. To reach a good prediction, we introduce several new visual analysis techniques of smoothing, multi-scaling, and weighted average with the involvement of human expert knowledge. We combine them into a well-fitted method to perform prediction.
Hao, Ming +5 more
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Sequential Quantile Prediction of Time Series [PDF]
IEEE Transactions on Information Theory, 2011 Motivated by a broad range of potential applications, we address the quantile prediction problem of real-valued time series. We present a sequential quantile forecasting model based on the combination of a set of elementary nearest neighbor-type predictors called "experts" and show its consistency under a minimum of conditions.
Biau, Gérard, Patra, B.
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PREDICTION OF INCOMING ORDERS USING THE LONG SHORT-TERM MEMORY METHOD AT PT. XYZ
JISA (Jurnal Informatika dan Sains), 2021 Currently the need for domestic packaging paper continues to increase, driven by the level of consumer awareness about sustainable packaging. PT XYZ is a local company engaged in the Corrugated Cardboard Box (KKG) industry.
Lukman Irawan +2 more
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