Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/123119
Title: Financial Time Series Forecasting: A Data Stream Mining-Based System
Authors: Bousbaa, Zineb
Sánchez Medina, Javier Jesús 
Bencharef, Omar
UNESCO Clasification: 120312 Bancos de datos
Keywords: Adaptive learning
Data stream mining
Financial time series forecasting
Incremental learning
Online learning
Issue Date: 2023
Journal: Electronics (Switzerland) 
Abstract: Data stream mining (DSM) represents a promising process to forecast financial time series exchange rate. Financial historical data generate several types of cyclical patterns that evolve, grow, decrease, and end up dying. Within historical data, we can notice long-term, seasonal, and irregular trends. All these changes make traditional static machine learning models not relevant to those study cases. The statistically unstable evolution of financial market behavior yields a progressive deterioration in any trained static model. Those models do not provide the required characteristics to evolve continuously and sustain good forecasting performance as the data distribution changes. Online learning without DSM mechanisms can also miss sudden or quick changes. In this paper, we propose a possible DSM methodology, trying to cope with that instability by implementing an incremental and adaptive strategy. The proposed algorithm includes the online Stochastic Gradient Descent algorithm (SGD), whose weights are optimized using the Particle Swarm Optimization Metaheuristic (PSO) to identify repetitive chart patterns in the FOREX historical data by forecasting the EUR/USD pair’s future values. The data trend change is detected using a statistical technique that studies if the received time series instances are stationary or not. Therefore, the sliding window size is minimized as changes are detected and maximized as the distribution becomes more stable. Results, though preliminary, show that the model prediction is better using flexible sliding windows that adapt according to the detected distribution changes using stationarity compared to learning using a fixed window size that does not incorporate any techniques for detecting and responding to pattern shifts.
URI: http://hdl.handle.net/10553/123119
ISSN: 2079-9292
DOI: 10.3390/electronics12092039
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