A Hybrid Model to Forecast Financial Time Series Based on Technical Analysis and Support Vector Machines - Benkerroum, Houda; Cherif, Walid; Kissi, Cherif (International Journal of Engineering and Technology, 2019)
The aim of this paper is to find functional relations in the behaviour of the USD swaps daily level time series and, in turn, forecast future values of the series through applying a relevant machine learning technique. As our original dataset variables appeared to show strong cross correlation, we decided to use Principal Component Analysis (PCA) to process the data before passing it to our machine learning algorithm. Then, we extract some technical indicators from our historical product price time series and use them as inputs to our model. Finally, Support Vector Machines (SVMs) is applied to our processed data set to realise the forecasting, and the resulting time series can be used to generate signals of when to enter or unwind a trade. Analysis of the results demonstrates that it is advantageous to apply SVMs to forecast financial time series, based on the criteria of Root Mean Square Error (RMSE) and F-measure (F score)Â Â