Financial Time Series Forecasting: A Data Stream Mining-Based System

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.

A Time-Series-Based Sample Amplification Model for Data Stream with Sparse Samples

“…For forecasting financial time series, Bousbaa et al [30] established data stream mining approaches. They used FOREX historical financial data to predict future values using a Particle Swarm Optimization (PSO) approach.…”

Advanced Machine Learning Applications in Big Data Analytics

“…Experimental results showed that CEEMD-MultiRocket ranked second in classification accuracy on the 109 datasets from the UCR repository against a spread of state-of-the-art TSC models, only behind HIVE-COTE 2.0, but with only 1.4% of the latter's computing load. Bousbaa et al [4] proposed an incremental and adaptive strategy using the online stochastic gradient descent algorithm (SGD) and particle swarm optimization metaheuristic (PSO). Two techniques were involved in data stream mining (DSM): adaptive sliding windows and change detection.…”

Time Series Analysis and Algorithm for Fluctuation Pattern Recognition and Forecasting of Economic Indicators

“…The use of ANN algorithms in economic forecasting deviates from traditional methodologies, providing a datadriven methodology capable of adjusting to the complexities and uncertainties of real-world economic systems [5]. ANNs can use the large amounts of historical data available to make forecasts that account for the dynamic character of economic variables [6]. Furthermore, the intrinsic flexibility of ANN topologies enables the incorporation of a wide range of data sources, including macroeconomic variables, financial market data, and sociopolitical elements, thereby increasing the depth and accuracy of forecasting [7].…”