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Time Series Data Forecasting and Analysis:


Time Series Data Analysis is a analysis techniques which use the different time series data to extract the require desirable information. The results of the time series data analysis normally available in the graphical format for presentation. We can denote them with the tide or sunspot graphical representation. The Complete time series data analysis gives us the different verities and techniques to make the analysis of the data of the given variable in the different time periods. There are some common techniques which we are using in the time series data analysis are as follows:
·         Simple moving average.
·         Weighted moving average.
·         Exponential smoothing method.
The explanations of these above mentioned time series based data analysis are as follows.

Simple Moving Average:
            According to the first method of Simple moving average. When we have a complete or large data and there are no seasonal variations or changes in it and the data have the variation just according to the normal factors like variations in sales during a financial year for every month. In this technique we take a sum of some months like in a year there are 3 quarters so we take the sum of the 4 months each. For example from January to April we sum the available sales figure and then divide the results by four numbers of months the result will be known as the foretasted figure. Later on we can abstract the error function form it and also take the error percentage form it. This process is repeated for the successive group of time periods.

Weighted Moving Average:
            Weighted moving average method is a method in which we give a proper weight age or importance and through that way we calculate the forecast for that period of time. Like for example to calculated the sales for the month up coming months we give some weight age to like to calculate the January sales forecast figures we start form the last year September to December and give each month sales a proper weight percentage and calculate it with the concerned month sales at last till December we calculate all the available new forecast and make a sum of all of them for the January month sales. To extract the error and error percentage we use the same formula which is as common and as follows:
                Error =         Forecast Sales – Actual Sales
            Error % =         No of Months / Error
 Then multiplied by 100 to convert the amount in %.

Exponential Smoothing:
            The Exponential Smoothing is another method of time series data analysis and making the forecast. It is consist of three different steps in which we develop some forecasts, then set the smoothing factor and at last to calculate the next period forecast. The extraction of error and error % is same for it like the other methods. The Exponential Smoothing normally using for the short term data forecasting. The Formula which is normally using for the exponential smoothing is as follows:
            F t          =             (α) x t-1 + (1- α)F t-1
            Here F t = Consider as the Forecast time figure.
                        t= time period
                        α= the Smoothing factor which is always is less than 1 and greater than 0.
                        X= the actual result for a period.


There are the some of the methods which helps us to calculate the Time Series Data Analysis from some given periods.

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