Stock market forecasting using empirical mode decomposition with holt-winter

Awajan, Ahmad M.; Ismail, Mohd Tahir; Wadi, S. Al

doi:10.1063/1.5136394

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…EMD is an adaptive decomposition method effective for nonstationary and nonlinear time series data (Awajan et al, 2019; Leung & Zhao, 2021). For any time‐series

X ((), t)

observed over time [0, T], EMD decomposes the signals into a finite sequence of oscillating components iteratively called intrinsic mode functions (IMFs) and a nonoscillatory trend called the residue term

r ((), t)

.…”

Section: Model Formulationmentioning

confidence: 99%

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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“…EMD is an adaptive decomposition method effective for nonstationary and nonlinear time series data (Awajan et al, 2019; Leung & Zhao, 2021). For any time‐series

X ((), t)

r ((), t)

.…”

Section: Model Formulationmentioning

confidence: 99%

Electricity price forecasting using hybrid deep learned networks

Singh

2023

Journal of Forecasting

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“…The components are cash profit forecast, growth rate, and revenue growth. Awajan et al (2019) used a combined method, including empirical mode decomposition and the Holt-Winters forecasting method, to predict the stock market index of Brazil, Indonesia, and India. Noemi et al ( 2018) used a combined support vector regression approach and empirical mode decomposition to forecast stock prices.…”

Section: Ramos Et Al (mentioning

confidence: 99%

Pairs Trading Based on Empirical Mode Decomposition (EMD)

Zarintaj,

Aghasi,

Baktash

2023

IJFIFSA

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As a trading strategy, pairs trading is performed based on the arbitrage opportunities extracted from statistical models. It is an outcome of the distance between an asset pair and the equilibrium state. Consequently, selecting a pair with the potential to form long-term relationships and reverting to the mean is the main challenge associated with pair trading. Cointegration is one of the most famous statistical tests for selecting a pair's trading. The present study uses Empirical Mode Decomposition (EMD) to decompose the time series of 96

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Stock market forecasting using empirical mode decomposition with holt-winter

Cited by 2 publications

References 4 publications

Electricity price forecasting using hybrid deep learned networks

Electricity price forecasting using hybrid deep learned networks

Pairs Trading Based on Empirical Mode Decomposition (EMD)

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