Feature Selection using Complex Networks to Support Price Trend Forecast in Energy Markets

Author(s):Show less -

Castilho, Douglas ; Santos, Moises R. ; Tinos, Renato ; Carvalho, Andre C. P. L. F. ; Paula, Marcos B. S. ; Ladeira, Lucas ; Guarnier, Ewerton ; Silva Filho, Donato ; Suiama, Danilo Y. ; Junior, Edmur A. M. ; Alipio, Lucas P. ; IEEE

• Document type: Journal article
• Source: 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN; v. N/A, p. 9-pg., 2023-01-01.

Abstract

Machine learning algorithms have been increasingly used to solve problems related to financial markets. Predicting the price or trends of assets in different markets is the subject of several studies. As other emerging markets, the Brazilian energy market has grown in number of traded volume and players, providing a business environment that can be explored through price trend forecasting techniques. The formation of prices of energy products depends on several factors. As the source of energy production in Brazil is predominantly through hydroelectric plants, information on water storage and natural flow between hydroelectric plants can directly impact price fluctuations. We propose a new strategy based on machine learning and complex networks for predicting the price trend in energy markets. The new strategy uses storage and flow data from the most important water stations in a hydroelectric power network. For this, we developed a feature selection method based on complex networks to identify and filter the main storage stations of the Brazilian water network. Data from the selected stations are then used as input for machine learning algorithms. We use three methods to extract centrality metrics from storage stations. Experimental results indicate that better performance is obtained by the proposed strategy, when compared to two baseline methods using different machine learning algorithms. The better performance is confirmed by a financial analysis of the results when simulating the investment strategies using algotrading. (AU)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 19/10012-2 - Meta-learning for time-series forecasting
• Grantee: Moisés Rocha dos Santos
• Support Opportunities: Scholarships in Brazil - Doctorate