LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction

Liang, Xiaodan; Ge, Zhaodi; Sun, Liling; He, Maowei; Chen, Hanning

doi:10.1155/2019/1340174

Cited by 62 publications

(43 citation statements)

References 13 publications

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“…Their outputs are calculated as follows: The input gate decides which information in the cell states needs to be updated, and the output gate decides which part of the information in the cell states will be output. The forget gate decides which information should be dropped from the cell state to reset the partial memory [30]. In this way, LSTM has the option of removing or adding information to the cell state rather than fully overwriting cell states as done by standard RNNs [28].…”

Section: Long Short-term Memory (Lstm) Algorithmmentioning

confidence: 99%

“…Then, several coefficients of wavelets are obtained. The high-frequency information or low-frequency information of the signals are obtained through high-pass or low-pass filters, respectively [30,31]. Let us say that n is the sensor signal length; then the noised signal, Y, is expressed as follows:…”

Section: Discrete Waveform Transform (Dwt)mentioning

confidence: 99%

“…In the soft-threshold de-noising method, when the wavelet coefficient y < λ, the noise can be reset to zero, while when y ≥ λ, the |y| is subtracted by λ. The soft-threshold de-noising method is expressed as follows [30]:…”

Section: Discrete Waveform Transform (Dwt)mentioning

confidence: 99%

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Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network

Manie

Peng

et al. 2020

Sensors

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In this paper, for an intensity wavelength division multiplexing (IWDM)-based multipoint fiber Bragg grating (FBG) sensor network, an effective strain sensing signal measurement method, called a long short-term memory (LSTM) machine learning algorithm, integrated with data de-noising techniques is proposed. These are considered extremely accurate for the prediction of very complex problems. Four ports of an optical coupler with distinct output power ratios of 70%, 60%, 40%, and 30% have been used in the proposed distributed IWDM-based FBG sensor network to connect a number of FBG sensors for strain sensing. In an IWDM-based FBG sensor network, distinct power ratios of coupler ports can contain distinct powers or intensities. However, unstable output power in the sensor system due to random noise, harsh environments, aging of the equipment, or other environmental factors can introduce fluctuations and noise to the spectra of the FBGs, which makes it hard to distinguish the sensing signals of FBGs from the noise signals. As a result, noise reduction and signal processing methods play a significant role in enhancing the capability of strain sensing. Thus, to reduce the noise, to improve the signal-to-noise ratio, and to accurately measure the sensing signal of FBGs, we proposed a long short-term memory (LSTM) deep learning algorithm integrated with discrete waveform transform (DWT) data smoother (de-noising) techniques. The DWT data de-noising methods are important techniques for analyzing and de-noising the sensor signals, and it further improves the strain sensing signal measurement accuracy of the LSTM model. Thus, after de-noising the sensor data, these data are fed into the LSTM model to measure the sensing signal of each FBG. The experimental results prove that the integration of LSTM with the DWT data de-noising technique achieved better sensing signal measurement accuracy, even in noisy data or environments. Therefore, the proposed IWDM-based FBG sensor network can accurately sense the signal of strain, even in bad or noisy environments; can increase the number of FBG sensors multiplexed in the sensor system; and can enhance the capacity of the sensor system.

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Section: Long Short-term Memory (Lstm) Algorithmmentioning

confidence: 99%

Section: Discrete Waveform Transform (Dwt)mentioning

confidence: 99%

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Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network

Manie

Peng

et al. 2020

Sensors

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“…(3) Construct the LSSVM model by equation 21, evaluate the fitness values of the ants and antlions by equation 34; (4) Determine the antlion with the minimum fitness value as the elite antlion; (5) While t < t max do (6) t � t + 1; (7) For each ant (8) Select an antlion based on the fitness value using the Roulette wheel principle; (9) Update the lower and upper bounds by equation 38; (10) Calculate the bounds around the selected antlion by equation 37; (11) Determine R t A and R t E , the Levy flights around the selected antlion by equations (31) and 41; (12) Update the new state of the ants using equation 39 Step 1…”

Section: The Construction Process Of the Dam Deformation Prediction Mmentioning

confidence: 99%

“…To date, various powerful prediction models have been applied in complex nonlinearity and optimization problems in pump turbine characteristics identified [7], flood interval prediction [8], stock price prediction [9], and wind speed forecasting [10]. In the field of dam deformation prediction based on prototypical observations, many prediction models have also been established, such as multiple linear regression [11], neural network [5], support vector machines [12], extreme learning machine [13], boosted regression trees [14], and Gaussian process regression [15].…”

Section: Introductionmentioning

confidence: 99%

An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction

Chen

Shao

et al. 2020

Mathematical Problems in Engineering

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A dam deformation prediction model based on adaptive weighted least squares support vector machines (AWLSSVM) coupled with modified Ant Lion Optimization (ALO) is proposed, which can be utilized to evaluate the operational states of concrete dams. First, the Ant Lion Optimizer, a novel metaheuristic algorithm, is used to determine the punishment factor and kernel width in the least squares support vector machine (LSSVM) model, which simulates the hunting process of antlions in nature. Second, aiming to solve the premature convergence phenomenon, Levy flight is introduced into the ALO to improve the global optimization ability. Third, according to the statistical characteristics of the datum error, an improved normal distribution weighting rule is applied to update the weighted value of data samples based on the learning result of the LSSVM model. Moreover, taking a concrete arch dam in China as an example, the horizontal displacement recorded by a pendulum is used as a study object. The accuracy and validity of the proposed model are verified and evaluated based on the four evaluating criteria, and the results of the proposed model are compared with those of well-established models. The simulation results demonstrate that the proposed model outperforms other models and effectively overcomes the influence of outliers on the performance of the model. It also has high prediction accuracy, produces excellent generalization performance, and can be a promising alternative technique for the analysis and prediction of dam deformation and other fields, including flood interval prediction, the stock price market, and wind speed forecasting.

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Electricity price forecasting using hybrid deep learned networks

Singh

2023

Journal of Forecasting

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This paper presents a novel hybrid model integrating maximal overlap discrete wavelet transform (MODWT) denoising and empirical mode decomposition (EMD) with sequence‐to‐sequence (seq2seq) long short‐term memory (LSTM) neural networks for day‐ahead electricity price forecasting. The nonstationary and nonlinear time series electricity price data are first denoised using MODWT. The resulting signal is decomposed into several intrinsic mode functions (IMF) with different resolutions by EMD. The extracted IMF is then introduced into seq2seq LSTM to obtain an aggregated predicted value for electricity price. The proposed method is examined using the Nord pool Elspot energy market data. Empirical results show that the proposed model outperformed the other forecasting models like LSTM and stacked LSTM. The performance measures indicate that data denoising can significantly improve the prediction stability and the generalization ability of the LSTM model.

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LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction

Cited by 62 publications

References 13 publications

Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network

Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network

An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction

Electricity price forecasting using hybrid deep learned networks

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