is always a slightly variation in weather conditions which may depend
upon the last seven days or so variation. Here variation refers to difference
between previous day parameter and present day’s parameter. Also there exists a
dependency between the weather condi-tions persisting in current week in
consideration and those of previous years. In this work a methodology is being
proposed that could mathematically model these two types of depen-dency and
utilize them to predict the future’s weather condi-tions. To predict the day’s
weather conditions this work will take into account the conditions prevailing
in previous week, that is, in last seven days which are assumed to be known.
Also the weather condition of seven previous days and seven upcoming days for
previous year is taken into consideration. For instance if the weather
condition of 16 November 2012 is to be predicted then we will take into
consideration the conditions from 09 November 2012 to 15 November 2012 and
conditions from 09 November to 22 November 2011 for previ-ous years. Now in
order to model the aforesaid dependencies the current year’s variation
throughout the week is being matched with those of previous years by making use
of sliding window. T he best-matched window is selected to make the prediction.
The selected window and the current year’s weekly variations are together used
to predict the weather condition. The reason for applying sliding window
matching is that the weather conditions prevailing in a year may not lie or
fall on exactly the same date as they might have existed in previous years.
That is why seven previous days and seven ongoing days are being considered.
Hence a total period of fortnight is checked in previous condition to find the
similar one. Sliding window is quite good technique to capture the variation
that could match the current year’s variation.
Window Algorithm. The work proposes to predict a day’s
weather conditions. For this the previous seven days weather is taken into
consideration along with fortnight weather conditions of past years. Suppose we
need to predict