Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Yang, Ke; Liu, Yiliu; Yao, Yunan; Fan, Shidong; Mosleh, Ali

doi:10.1016/j.jmsy.2020.11.020

Cited by 16 publications

(5 citation statements)

References 34 publications

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“… Stock market data contain noise that affects forecast results. Methods, such as wavelet denoising [ 27 ] and principal component analysis [ 28 ], can eliminate the influence of irrelevant factors and improve the prediction effect to a certain extent. Time series analysis has been applied in fields, such as natural science [ 29 ] and industrial time series prediction [ 30 ].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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Stock Index Prediction Based on Time Series Decomposition and Hybrid Model

et al. 2022

Entropy

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The stock index is an important indicator to measure stock market fluctuation, with a guiding role for investors’ decision-making, thus being the object of much research. However, the stock market is affected by uncertainty and volatility, making accurate prediction a challenging task. We propose a new stock index forecasting model based on time series decomposition and a hybrid model. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the stock index into a series of Intrinsic Mode Functions (IMFs) with different feature scales and trend term. The Augmented Dickey Fuller (ADF) method judges the stability of each IMFs and trend term. The Autoregressive Moving Average (ARMA) model is used on stationary time series, and a Long Short-Term Memory (LSTM) model extracts abstract features of unstable time series. The predicted results of each time sequence are reconstructed to obtain the final predicted value. Experiments are conducted on four stock index time series, and the results show that the prediction of the proposed model is closer to the real value than that of seven reference models, and has a good quantitative investment reference value.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Conclusion and Discussionmentioning

confidence: 99%

Stock Index Prediction Based on Time Series Decomposition and Hybrid Model

et al. 2022

Entropy

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“…Standard recurrent neural architectures, like LSTM, treat the inputs in one direction only and ignore the possessed information about the future. The bi-directional LSTM (Bi-LSTM) model responds to this issue in its operating process [22].…”

Section: Methods 21 Principle Of Lstm and Bi-lstm Structuresmentioning

confidence: 99%

Efficiency of recurrent neural networks for seasonal trended time series modelling

Abassi,

Idrais,

Sabour

2023

IJECE

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Seasonal time series with trends are the most common data sets used in forecasting. This work focuses on the automatic processing of a non-pre-processed time series by studying the efficiency of recurrent neural networks (RNN), in particular both long short-term memory (LSTM), and bidirectional long short-term memory (Bi-LSTM) extensions, for modelling seasonal time series with trend. For this purpose, we are interested in the learning stability of the established systems using the mean average percentage error (MAPE) as a measure. Both simulated and real data were examined, and we have found a positive correlation between the signal period and the system input vector length for stable and relatively efficient learning. We also examined the white noise impact on the learning performance.

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“…With all gradients calculated according to the corresponding error term (loss function), the weights associated with input gate, output gate, and forget gate are updated. More details about the back-propagation process of the LSTM can be refereed in the literature [35].…”

Section: Long-short Term Memorymentioning

confidence: 99%

A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines

2022

Renewable Energy

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Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Cited by 16 publications

References 34 publications

Stock Index Prediction Based on Time Series Decomposition and Hybrid Model

Stock Index Prediction Based on Time Series Decomposition and Hybrid Model

Efficiency of recurrent neural networks for seasonal trended time series modelling

A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines

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