Pigeon-inspired optimization and extreme learning machine via wavelet packet analysis for predicting bulk commodity futures prices

Jiang, Feng; He, Jiaqi; Zeng, Zhigang

doi:10.1007/s11432-018-9714-5

Cited by 40 publications

(14 citation statements)

References 24 publications

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“…Proposed by Huang et al (), the extreme learning machine (ELM) method is a type of single‐hidden‐layer intelligent neural network and has favorable performance in classification, clustering, and regression tasks (Feng et al, ; Huang et al, ; Jiang et al, ). The weights that connect the input features to hidden nodes of the ELM model are randomly assigned and never updated (Kumar et al, ).…”

Section: Methodsmentioning

confidence: 99%

A hybrid model considering cointegration for interval‐valued pork price forecasting in China

Zhang

Mugera

et al. 2020

Journal of Forecasting

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Compared with point forecasting, interval forecasting is believed to be more effective and helpful in decision making, as it provides more information about the data generation process. Based on the well-established "linear and nonlinear" modeling framework, a hybrid model is proposed by coupling the vector error correction model (VECM) with artificial intelligence models which consider the cointegration relationship between the lower and upper bounds (Coin-AIs). VECM is first employed to fit the original time series with the residual error series modeled by Coin-AIs. Using pork price as a research sample, the empirical results statistically confirm the superiority of the proposed VECM-CoinAIs over other competing models, which include six single models and six hybrid models. This result suggests that considering the cointegration relationship is a workable direction for improving the forecast performance of the interval-valued time series. Moreover, with a reasonable data transformation process, interval forecasting is proven to be more accurate than point forecasting.

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Section: Methodsmentioning

confidence: 99%

A hybrid model considering cointegration for interval‐valued pork price forecasting in China

Zhang

Mugera

et al. 2020

Journal of Forecasting

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“…Jiang et al proposed a hybrid approach consisting of pigeon-inspired optimization (PIO) and ELM based on wavelet packet analysis (WPA) for predicting bulk commodity prices. That hybrid model possessed a better performance on horizontal precision, directional precision and robustness [ 34 ]. Khuwaja et al presented a framework to predict the stock price movement using phase space reconstruction (PSR) and ELM, and results achieved from the proposed framework were compared with the conventional machine learning pipeline, as well as the baseline methods [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

Wang

2021

Entropy

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The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).

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“…Compreender as tendências dos preços tornam-se um pré-requisito para que os formuladores de políticas implementem diretrizes de subsídios aos produtos agrícolas e ao consumidor. Nessa perspectiva, muitos trabalhos têm sido propostos para predizer os preços futuros de commodities agrícolas (Ayankoya et al, 2016;Wang et al, 2017;Zhang et al, 2018;Puchalsky et al, 2018;Jiang et al, 2019). No entanto, realizar a predição em um cenário real de mercado é uma das aplicações mais desafiantes devido a sua natureza complexa, dinâmica e não linear Sezer et al (2020a).…”

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Previsão do preço futuro de commodities agrícolas: um estudo para enriquecer séries temporais

Filho

Marcacini

Rezende

2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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Products derived from corn and soy are consumed on a large scale in the world. Market price fluctuations have far-reaching effects on grain consumers, criteria and indices. Thus, the forecast of future grain and grain prices has attracted the attention of investments and agribusiness companies. Estimation, forecasting models use time series to predict future values. However, external factors can originate the data in time series, such as political events, improvement patterns and the foreign exchange market. This information is not explicit in time series data and can make it difficult to predict variable values. Textual data extracted from news, forums and social media can be a source of knowledge about external factors and potentially useful for weather forecasting models. Some studies present text mining techniques to combine textual data with time series. However, existing representations have limitations, such as the curse of dimensionality and ineffective attributes. In this sense, this work proposes representations of time series enriched with textual information. The results indicate that the methods used can be an alternative to improve the prediction performance in regression tasks. Resumo: Os produtos derivados do milho e da soja são consumidos em grande escala no mundo. As flutuações dos preços no mercado têm efeitos de longo alcance sobre os consumidores, agricultores e processadores de grãos. Assim, a previsão de preços futuros desses grãos tem atraído significativa atenção dos pesquisadores e empresas do agronegócio. Geralmente, os modelos de previsão usam séries temporais para predizer valores futuros. No entanto, fatores externos podem influenciar os dados em séries temporais, como eventos políticos, crises econômicas e o mercado de câmbio. Essas informações não são explícitas nos dados da série temporal e podem dificultar a previsão dos valores das variáveis. Os dados textuais extraídos de notícias, fóruns e redes sociais podem ser uma fonte de conhecimento sobre fatores externos e potencialmente úteis para modelos de previsão de séries temporais. Alguns estudos apresentam técnicas de mineração de texto para combinar dados textuais com séries temporais. No entanto, as representações existentes apresentam algumas limitações, como a maldição da dimensionalidade e atributos ineficazes. Nesse sentido, este trabalho propõem representações de séries temporais enriquecidas com informações textuais. Os resultados indicam que os métodos utilizados podem ser uma alternativa para melhorar o desempenho de previsão em tarefas de regressão.

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Pigeon-inspired optimization and extreme learning machine via wavelet packet analysis for predicting bulk commodity futures prices

Cited by 40 publications

References 24 publications

A hybrid model considering cointegration for interval‐valued pork price forecasting in China

A hybrid model considering cointegration for interval‐valued pork price forecasting in China

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

Previsão do preço futuro de commodities agrícolas: um estudo para enriquecer séries temporais

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