A methodology for coffee price forecasting based on extreme learning machines
| Autor: | Hugo Siqueira, Marcella S. R. Martins, Carlos Henrique Rodrigues Alves, Sergio Luiz Stevan, Carolina Deina, Flavio Trojan, Matheus Henrique do Amaral Prates |
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| Rok vydání: | 2022 |
| Předmět: |
Artificial neural network
business.industry Computer science Exponential smoothing Forestry Filter (signal processing) Aquatic Science Machine learning computer.software_genre Partial autocorrelation function Computer Science Applications Autoregressive model Moving average Multilayer perceptron Animal Science and Zoology Autoregressive integrated moving average Artificial intelligence business Agronomy and Crop Science computer |
| Zdroj: | Information Processing in Agriculture. 9:556-565 |
| ISSN: | 2214-3173 |
| DOI: | 10.1016/j.inpa.2021.07.003 |
| Popis: | This work introduces a methodology to estimate coffee prices based on the use of Extreme Learning Machines. The process is initiated by identifying the presence of nonstationary components, like seasonality and trend. These components are withdrawn if they are found. Next, the temporal lags are selected based on the response of the Partial Autocorrelation Function filter. As predictors, we address the following models: Exponential Smoothing (ES), Autoregressive (AR) and Autoregressive Integrated and Moving Average (ARIMA) models, Multilayer Perceptron (MLP) and Extreme Learning Machines (ELMs) neural networks. The computational results based on three error metrics and two coffee types (Arabica and Robusta) showed that the neural networks, especially the ELM, can reach higher performance levels than the other models. The methodology, which presents preprocessing stages, lag selection, and use of ELM, is a novelty that contributes to the coffee prices forecasting field. |
| Databáze: | OpenAIRE |
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