Abstract:
Forecasting is the prediction of values of a variable based on known past
values of that variable or other related variables. Forecasts also may be based
on expert judgments which in turn are based on historical data and
experience. Artificial Neural Network (ANN) is a powerful data mining tool
that can capture and represent complex input-output relationships. In this
study, the forecasting ability of ANN model with standard Back-propagation
learning algorithm and trial-and-error observations in time series data has
been analyzed based on gold price in Sri Lanka for a period of three years
from 2014 to 2017. Comparing ANN model output and actual output via
plots and measuring Mean Square Error (MSE), we conclude that number of
inputs effects to the time series data forecasting in an ANN model and
forecasting ability decreases with increasing the number of forecasting days.
Therefore, nth
-day forecasting by changing the input-output data patterns: past
30 day values and nth
-future day value as input and output data respectively, is
better if need to forecast several future days.
