Question

This passge below require critical analysis and breakdown

Moving averages, weighted moving averages, and exponential smoothing methods are forecasting methods that are used for a time series with a horizontal pattern. These methods provide a high level of accuracy for short-range forecasts, such as a forecast for the next time period and are used to smooth out random fluctuations in the time series and have a high level of accuracy for short-range forecast. The moving Average method uses the average of most recent data values in the time series as the forecast for the next period. In moving averages, only the new observations are used; therefore, the old observations are replaced by the new averages; hence, claiming the name “moving” averages. (Anderson et al., 2016) Simple moving average is the easiest and simplest way to calculate forecasting and was used prior to the invention of computers. Moving averages typically use daily closing prices. (Banton, 2019) Weighted moving average involves selecting a different weight for each data value in the moving average. More weight is given to the most recent observations, as the observation becomes old, the relevant weight decreases. Exponential smoothing also uses a weighted average of past time series values as a forecast and is used a special case of the weighted moving averages method.

There are several measures to determine the forecast accuracy. “These measures are used to determine how well a particular forecasting method is able to reproduce the time series data that are already available. The forecast error is used to measure the forecast accuracy. The “mean absolute error, denoted MAE, is a measure of forecast accuracy that avoids the problem of positive and negative forecast errors offsetting one another...MAE is the average of the absolute value of the forecast errors.” (Anderson, et al., 2016) Mean Squared error is another measure that avoids the problem of positive and negative errors offsetting each other. This is obtained by computing the average of the squared forecast errors. (Anderson, et al., 2016) The accuracy of the forecast makes the prediction more reliable and provides stability in planning, purchasing and forecasting productions.

Answer 1

CRITICAL ANALYSIS AND BREAKDOWN

Time series which means that the data is in a series of particular time periods or intervals basically uses 3 methods mainly in its horizontal pattern, namely :- Moving Averages; Weighted Moving Averages and Exponential Smoothing Methods.

These three methods are basically used for their high level accuracy and they deal or easily remove the random fluctuations in the time series.

Now firstly talking about the Moving Average Method.
This method basically involves creating a new series where the values are comprised of the average of raw observations in the current period. Here only the new observations are made or say the old ones are replaced by the new ones. Simple moving averages is the easiest and most frequent used method prior to the invention of computers.

Now talking about the Weighted Moving Average. This method basically puts more weight on recent data and less on the past data. This is done by multiplying each bar's price by a weighting factor.

Now lets have a look on exponential smoothing method. It assigns exponentially decreasing weights for newest to oldest observations.

Now various methods take place to ensure whether the forecast is accurate enough or not. These measures basically determine how accurately a particular forecasting method reproduces a time series data. One such measure used is MAE (Mean Absolute Error) :- It is the average of the absolute value of forecast errors. Another such measure used is Mean Squared Error :- This basically computes the average of the squared forecast errors.

It may be concluded that the accuracy of forecasting is very crucial as it provides stability everywhere be it in planning or purchasing or in forecasting productions.