Moving Average Method: Limitations and Types of

mournful Average Method Limitations and Types ofForecasting is very essential and important break in business planning. It refers to estimation of the invite for products and services in coming future and the resource necessity to produce these outputs. Estimates of the future demand for products or services are commonly referred to as sales forecast. In other words, forecasting is the art and science of predicting future events. Is is not mere a guess or prediction about the future without any rational basis. It may involve fetching historic entropy or intuitive prediction in the absence of historical data.Basis of ForecastingForecasting by its nature uses data from the past stop consonant to forecast the future projection of the company. Historical data includes your brass sections financial statements and any information you believe has relative predictive value to the future victor of your company. Historical data doesnt have to solely come from your company it can also be historical macro scotch data, such as the Consumer Confidence Index, interest rates, housing starts or any other economic variable you believe has an effect on your business based on your business experience and observations.Moving Average MethodA pitiable just system uses a count of most novel historical actual data set to generate a forecast. The lamentable sightly for n emergence of occlusions in the moving reasonable is figured as This method uses the come of a number of abutting data points or periods. The averaging process uses overlapping observations to generate modal(a)s. The landmark moving refers to the way cleans are manoeuvre the forecast moves up or down the magazine series to pick observations to calculate an honest of a fixed number of observations. In our ten periods on the question the moving comelys method would use the average of the most juvenile ten observations of the data in the time series as the forecast for the near period.The mov ing average is commonly utilise with time series data to smooth out the curtly-run fluctuation and highlights long term trends or wheels. The threshold between Long-term and short-term term depends on the application and the parameter of the moving average will be set accordingly. For example it usually utilise in the technical analysis of financial data like descent damages and return various stock or trading volume A moving average also called rolling average, is an average price movement indicator, showing average value of the data within specific time frame.Moving average levels are interpreted as resistance in a rising market, or defend in a falling market. Here a support level means a price mark where the price tends to find support as it is going down. The price is more likely to bounce shoot this level rather than break through it. A resistance level is the opposite of a support level and is an upper extreme where the price tends to find resistance as it is going up.Modern pictorial analytic programs calculate wide range of different Moving Average types and offer assortment of their visualisation styles. A time frame for calculation could be set as short, intermediate or long term. For long term trend the 200- years average is most popular for medium term 50-days average and for short term 10 days average. Following types of rolling averages are used more often than others a simple moving average (SMA) a freighted moving average (WMA) and an exponentially moving average (EMA).Types of moving average methodSimple moving average method it is used to estimate the average of a demand time series and remove the effects of random fluctuation. It is most useful when demand has no pronounced trend or seasonal worker fluctuations. In this method if we use n period moving averages, the average demand for the n most recent time periods is calculated and used as forecast for the next time period. For the next period, after the demand is known, the older demand from the previous average is replaced with the most recent demand and the average is recalculated.Weighted moving average method in this method each historical demand in the moving average can have its own weight and the sum of the weight equals one. For example, in a 5 period weighted moving average model, the most recent period might be assigned a weight 0.50, the second most recent period might be assigned a weight of 0.30, 0.20, 0.10, and for third most period with a weight of 0.05.textWMA_M = n p_M + (n-1) p_M-1 + cdots + 2 p_(M-n+2) + p_(M-n+1) over n + (n-1) + cdots + 2 + 1The advantage of weighted average method is that is allows emphasis on recent demand over primarily demand.Exponential Smoothing Method It is a sophisticated weighted moving method that is still relatively blowzy to understand and use. It requires only three items of data periods forecast, the actual demand for this period and which is referred to as smoothing constant and having a value be tween 0 and 1. The formula of the ESM is as followsFt = Ft-1 + (At-1 Ft-1)Where Ft = Forecast for the period (t)Ft-1 = Forecast for the previous period (t-1)At-1 = Actual demand for the previous period (t-1) = Smoothing constant (value varies from 0 to 1)Selecting a smoothing constant is basically a matter of judgment or trial and error.Commonly used values of range from 0.05 to 0.5.Feature Moving average method-Smoothing data Moving average help in smoothing or smooth function on the original sequence, the original sequence of fluctuation is weakened, and the average breakup number N bigger, stronger on series smoothing effect.Odd and Even Moving average time legal separation number N is odd, only a moving average, the moving average as the middle moving average terms in a trend representative value and when the moving average term N is even, the moving average value represents the middle position of the even level, not on a time, is in need of an adjacent two average value of the moving average, it can make the average value of a certain period of time, this is called shift is average, also become the center of moving average. seasonal changes When the series include seasonal change, moving average interval number should be consistent with the seasonal variation of N length, in order to eliminate the seasonal variation if the sequence contains a cycle of change, from the terms N and cycle length should be basically the same average, cycle fluctuation reasoning by elimination can be betterAdvantages of Moving average methodEasily understandable The moving average model assumption is that the most accurate prediction of future demand is a simple (linear) confederacy of past demand moving average method is easy to understand than any other method. This method smooths the data and makes it easier to spot trend..Simple and Easy Calculation Moving average is calculated by taking the arithmetic mean of a given set of values. They are easier to use than other regression models. For example, to calculate a basic 10-day moving average you would add up the closing prices from the past 10 days and then divide the result by 10.Stable Forecasts how responsive we want the forecasting model to be to changes in the actual demand data must be balanced against our desire to suppress inapplicable chance variation or noise in the data. With help of moving average can contact such objectives.Limitations of Moving Average MethodEqual weighing is given to each of the values used in the moving average calculation, whereas it is reasonable most reasonable data is more important to watercourse situations.The moving average method doesnot takes into account the data outside the average period.The use of unadjusted moving average can lead to misguiding forecastThe moving average method to a large number of data records from the pastThrough the introduction of new data is more and more time, continuously rewrite average value, as predicted value.The basic principle of moving average method is through the moving average to eliminate irregular time series of changes and other changes, thus revealing the long-term trend of time series.Solution to the Given ProblemYearNo. of Cars3 Year Moving Total3 Year Moving Average11324________21605________3148644151471.674156746581552.675168747401580.006102142751425.007142441321377.33898634311143.679152939391313.0010142539401313.33