Based on Ruby version
It calculates the initial values and returns the forecast for m periods.
# y Time series array
# alpha Level smoothing coefficient
# beta Trend smoothing coefficient (increasing beta tightens fit)
# gamma Seasonal smoothing coefficient
# period A complete season's data consists of L periods. And we need
# to estimate the trend factor from one period to the next. To
# accomplish this, it is advisable to use two complete seasons;
# that is, 2L periods.
# m Extrapolated future data points
# - 4 quarterly
# - 7 weekly
# - 12 monthly
Frecast(y, alpha, beta, gamma, period, m)
For details, see: http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc43.htm http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc435.htm