A novel model for chaotic complex time series with large of data forecasting

Li, Pengcheng; Zhang, Fei; Gao, Lu; Liu, Yongqian; Ren, Xiaoying

doi:10.1016/j.knosys.2021.107009

Cited by 10 publications

(3 citation statements)

References 42 publications

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“…Time series prediction is a field of research with increasing interest that is broadly used in various applications such as economy, bio-medicine, engineering, astronomy, weather forecast, air traffic management. The purpose of time series prediction is to predict the future state of a dynamic system from the observation of previous states [1]. However, in a significant number of prediction problems, we have to face uncertainty, non-linearity, chaotic behaviors and non-stationarity, which deteriorates the prediction accuracy of the model.…”

Section: Introductionmentioning

confidence: 99%

“…They can be generally categorized into two types: the statistical approach and the deep learning approach. Statistical approaches such as SARIMA [2], Prophet [3] can predict time series precisely by exploiting the relationship between the original data and the predicted states while deep learning approaches such as LSTM, Transformer can model data with rich temporal patterns and learn high-level representations of features and associated nonlinear functions without relying on experts to select which of the manuallycrafted features to employ [1], [4].…”

Section: Introductionmentioning

confidence: 99%

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Applying Bayesian inference in a hybrid CNN-LSTM model for time-series prediction

Nghiem

et al. 2022

2022 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)

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Convolutional neural networks (CNN) and Long short-term memory (LSTM) provide stateof-the-art performance in various tasks. However, these models are faced with overfitting on small data and cannot measure uncertainty, which have a negative effect on their generalization abilities. In addition, the prediction task can face many challenges because of the complex long-term fluctuations, especially in time series datasets. Recently, applying Bayesian inference in deep learning to estimate the uncertainty in the model prediction was introduced. This approach can be highly robust to overfitting and allows to estimate uncertainty. In this paper, we propose a novel approach using Bayesian inference in a hybrid CNN-LSTM model called CNN-Bayes LSTM for time series prediction. The experiments have been conducted on two real time series datasets, namely sunspot and weather datasets. The experimental results show that the proposed CNN-Bayes LSTM model is more effective than other forecasting models in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as well as for uncertainty quantification.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying Bayesian inference in a hybrid CNN-LSTM model for time-series prediction

Nghiem

et al. 2022

2022 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)

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show abstract

“…Industrial monitoring data often present the characteristics of time series [10], which is conducive to forecasting the future change trend with machine learning. Many researchers have applied the method of machine learning to the processing of time series data [11,12]. The forecast of the future price of the stock market can also be regarded as the forecast of time series.…”

Section: Introductionmentioning

confidence: 99%

A hybrid deep learning model approach for performance index prediction of mechanical equipment

Hou¹,

Cao²

2022

Meas. Sci. Technol.

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To forecast the health status of mechanical equipment in industrial production, fault diagnosis systems need a fast and accurate algorithm to forecast the important performance indexes of mechanical equipment. According to the characteristics of time series, a composite variable wavelet transform, deep autoencoder and long short-term memory (CWD-LSTM) hybrid neural network forecast algorithm is proposed to carry out one-step forecast experiments on air compressor datasets. As one of the important indexes reflecting the performance of the air compressor, loading time is usually a parameter that the fault diagnosis system needs to forecast and analyze. The experimental results show that compared with the original neural network and other similar algorithms, the CWD-LSTM algorithm has obvious advantages in forecasting the loading time under a variety of detection indexes. More importantly, CWD-LSTM does not require a high update frequency of the neural network, and manufacturers do not need a frequent training model to ensure the reliability of forecast.

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FDNet: Focal Decomposed Network for efficient, robust and practical time series forecasting

Shen

Wei

Wang

et al. 2023

Knowledge-Based Systems

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A novel model for chaotic complex time series with large of data forecasting

Cited by 10 publications

References 42 publications

Applying Bayesian inference in a hybrid CNN-LSTM model for time-series prediction

Applying Bayesian inference in a hybrid CNN-LSTM model for time-series prediction

A hybrid deep learning model approach for performance index prediction of mechanical equipment

FDNet: Focal Decomposed Network for efficient, robust and practical time series forecasting

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