應用粒子群最佳化演算法於台幣外匯動態金融時間序列預測之行為分析

Abstract

時間序列被廣泛地運用在真實世界中，不過傳統分析方法卻因為穩態數列的假設而出現了動態環境的問題。過往的方法學，如ARCH, GARCH模型，雖然提出了時間序列具有時變性來解決這個問題，但在動態環境的議題上仍有缺陷；近年來所使用的人工智慧方法也有為人詬病之處。因為在培養模型的時候都會將資料分成訓練及測試兩個部分，卻未考量時間序列具有時變性，可能導致了偽迴歸現象。為了分析動態時間序列，一個能夠考量動態環境變化卻又不會受到樣本外問題影響的方法是必要的，由於粒子群最佳化演算法具有快速收斂及避免區域最佳解的特質，且廣泛地用在時間序列預測領域中。因此，我們提出修正後粒子群最佳化演算法模型來考量動態環境變化，並能夠充分運用粒子群最佳化演算法的優點，進行動態金融時間序列的預測。

Time series has been widely applied in the real world; traditional methods can hardly solve the dynamic environment issue resulting from the assumption of stationary process. Many traditional models and artificial intelligence technologies had been developed under this assumption, and adapted the dynamic environment based on the time-varying characteristic. But these models still has drawback of dividing the time series into training set and testing set when developing the models. It means the time-varying characteristic of these two sets did not be considered, and it might cause spurious regression phenomenon and result in misleading the statistic analysis. In order to forecast dynamic time series, a model which can consider the dynamic environment and conquer the out-of-sample problem is necessary. Particle swarm optimization (PSO) has the characteristics of fast-convergence and avoiding local optimal, also has been widely used in the time series forecasting. So,in this research, we proposed a modified PSO to consider the dynamic environment issue and use the advantage of PSO to proceed dynamic financial time series forecasting and behavior analysis.