New hybrid approach for short-term wind speed predictions based on preprocessing algorithm and optimization theory

Hu, Weicheng; Yang, Qingshan; Chen, Hua-Peng; Yuan, Ziting; Li, Chen; Shao, Shuai; Zhang, Jian

doi:10.1016/j.renene.2021.08.044

Cited by 36 publications

(4 citation statements)

References 52 publications

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“…For example, the divide-and-conquer approach has been used in several fields: oil prices ( Rădulescu et al, 2020 ; Wang et al, 2018 ) foreign currency exchange rate ( Jin et al, 2021 ; Lin, Chiu & Lin, 2012 ; Wang & Luo, 2021 ), stock market trend ( Cheng & Wei, 2014 ; Na & Kim, 2021 ; Stasiak, 2020 ; Wang & Luo, 2021 ), wind speed ( Hu et al, 2021 ; Wang et al, 2014 ; Xie et al, 2021 ), electronics sales ( Chen & Lu, 2021 ; Lu & Shao, 2012 ), healthcare ( Aileni, Rodica & Valderrama, 2016 ; Dwivedi et al, 2019 ; Singh, Dwivedi & Srivastava, 2020 ), and tourism market ( Chen, Lai & Yeh, 2012 ; Guerra-Montenegro et al, 2021 ; Tang et al, 2021 ). The hybrid EMD combined with the artificial neural network(ANN) method was applied to predict the first, second, and third steps moving forward wind speed time series ( Chen et al, 2021 ; Hu et al, 2021 ; Liu et al, 2012 ; Liu, Hara & Kita, 2021 ). Several predicting powers from low to high frequency and short-term to long-term trend elements were observed for analysis of the accuracy of EMD forecasting combined with ANN in the Baltic Exchange Dry Index ( Gavriilidis et al, 2021 ; Zeng & Qu, 2014 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical mode decomposition using deep learning model for financial market forecasting

Jin

Jin²,

Chen

2022

PeerJ Computer Science

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Financial market forecasting is an essential component of financial systems; however, predicting financial market trends is a challenging job due to noisy and non-stationary information. Deep learning is renowned for bringing out excellent abstract features from the huge volume of raw data without depending on prior knowledge, which is potentially fascinating in forecasting financial transactions. This article aims to propose a deep learning model that autonomously mines the statistical rules of data and guides the financial market transactions based on empirical mode decomposition (EMD) with back-propagation neural networks (BPNN). Through the characteristic time scale of data, the intrinsic wave pattern was obtained and then decomposed. Financial market transaction data were analyzed, optimized using PSO, and predicted. Combining the nonlinear and non-stationary financial time series can improve prediction accuracy. The predictive model of deep learning, based on the analysis of the massive financial trading data, can forecast the future trend of financial market price, forming a trading signal when particular confidence is satisfied. The empirical results show that the EMD-based deep learning model has an excellent predicting performance.

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Section: Literature Reviewmentioning

confidence: 99%

Empirical mode decomposition using deep learning model for financial market forecasting

Jin

Jin²,

Chen

2022

PeerJ Computer Science

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“…As a result, identifying from stock price time series using a well-defined technique may be beneficial to both short-term and long-term investors. Because the market is typically reverting in short-term ITH [31][32][33][34][35][36][37], traders using technical analysis fail to create significant returns [38]. Long-term ITH, on the other hand, can yield big returns or assist in deciding whether to leave a particular stock to minimise loss by analysing a company's financial health using basic characteristics [39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

The Cost Realization on Time Frame Shift of Securities

Sagarkumar Buyya, Baswaraj Gadgay, Shubhangi D C

2023

tjjpt

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Investors use a variety of investing methods depending on the time scales () of their short-term and long-term investment time horizons (ITH). The nature of the market varies greatly depending on the type of investment. Hurst exponents (H) and normalised variance (NV) techniques based on empirical mode decomposition (EMD) have been used to identify key features of the market over various time horizons. For the stock price's decomposed intrinsic mode functions (IMF), the values of H and NV have been estimated. We found H1 0.5 0.04 and H1 0.75 for the IMFs with time periods ranging from a few days to 3 months and 5 months, respectively. Two time series from the IMFs have been reconstructed based on the value of H1: a) Short-term time series [XST (t)] with H1 = 0.5.b) long-term time series [XLT (t)] with H1 0.75 and a time span of 5 months. The XST (t) and XLT (t) indicators reveal that market dynamics in short-term ITH are random and connected in long-term ITH. We also discovered that the NV is very low in short-term ITH and gradually increases in long-term ITH. The results also reveal that in the long run, stock prices are connected with the company's fundamental factors. The discovery may aid investors in developing investment and trading strategies for both short-term and long-term investment goals.

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“…A hybrid model is a combination of several single models through an optimization algorithm. It can inherit the advantages of every single model, thereby improving the prediction accuracy and stability (Guotai et al, 2017b ; Hu et al, 2021 ; Jiang et al, 2020 ; Khalilpourazari and Doulabi, 2021 ). In existing studies, the most commonly used optimization algorithms are the genetic algorithm (GA) (Yang et al, 2019 ), particle swarm optimization (PSO) (Ribeiro et al, 2021 ), ant lion optimization algorithm (ALO) (P. et al, 2018 ), frog-leaping algorithm (FLA) (He et al, 2021 ), and whale optimization algorithm (WOA) (Lin and Zhang, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm

et al. 2022

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Crude oil is the most important energy source in the world, and fluctuations in oil prices can significantly influence investors, companies, and governments. However, crude oil prices have numerous characteristics, including randomness, sudden structural changes, intrinsic nonlinearity, volatility, and chaotic nature. This makes the accurate forecasting of crude oil prices a difficult and challenging task. In this paper, a hybrid prediction model for crude oil futures prices is proposed, the accuracy and robustness of which are demonstrated via controlled experiments and sensitivity analysis. This study uses a new data denoising method for data processing to improve the accuracy and stability of the predictions of crude oil prices. Furthermore, the chaotic time-series prediction method, shallow neural networks, linear model prediction methods, and deep learning methods are adopted as submodels. The results of interval forecasts with narrow widths and high prediction accuracies are derived by introducing a confidence interval adjustment coefficient. The results of the simulation experiments indicate that the proposed hybrid prediction model exhibits higher accuracy and efficiency, as well as better robustness of the forecasting than the control models. In summary, the proposed forecasting framework can derive accurate point and interval forecasts and provide a valuable reference for the price forecasting of crude oil futures.

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New hybrid approach for short-term wind speed predictions based on preprocessing algorithm and optimization theory

Cited by 36 publications

References 52 publications

Empirical mode decomposition using deep learning model for financial market forecasting

Empirical mode decomposition using deep learning model for financial market forecasting

The Cost Realization on Time Frame Shift of Securities

Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm

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