PREDICTION OF TIME SERIES DATA USING ANT SYSTEM ALGORITHM (Case Study of Stock Price Prediction)

Prediction of time series data is often conducted in finance, biology, astronomy,
medical, meteorology, etc. The aim of this prediction is to obtain early clue about
the future condition so that precise response could be taken. This research would
examine one of time series data prediction problem in finance, i.e. prediction of
stock price fluctuation.
Two approximations commonly conducted by asset analysis expert in predicting
the stock price are technical analysis based on histories data and fundamental
analysis based on macroeconomic and the company condition. This thesis
develops a model clustering those two approximations. This research uses similar
sequence matching (SSM) method to investigate the pattern of histories data. SSM
method conducts pattern investigation and classification in previous data based on
pattern sample that has been determined, whereas Euclidean distance is used as
parameter to measure similarity. Furthermore, this research also uses max – min
ant system method to combine SSM method with fundamental factors i.e.
company condition, macroeconomic and non economic factor involved. There are
four significant aspects in building ant system method, i.e. conducting graph
construction representing the faced problem, developing heuristic function model
and transition rule, developing pheromone updating model, and method used as
discharge iteration criteria. Heuristic function implemented in ant system method
is a representation of influence of stock price fluctuation data in past and influence
of present condition i.e. measurement of company condition factor (price earning
ratio, dividend yield, etc), macroeconomic condition (inflation level, interest level,
oil price, etc), and non-economy condition (domestic condition, fluctuation of
foreign stock index, etc).
The outputs of this research are model and software to predict stock price. The
simulation which has been carried out shows satisfying result to predict pattern of
the stock price fluctuation and give tolerate error to predict return value/stock
price.