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A moving average could be a calculation to investigate data points by creating a series of averages of various subsets of the complete data set. It's also called a moving mean.

The calculation of 3-month moving average is calculated by using the following formula:

$\text{Simple moving average}=\frac{\left({\text{A}}_1+{\mathrm{A}}_2+..........+{\mathrm{A}}_{\text{n}}\right)}{\text{n}}$

$$\begin{gathered} =\frac{62+65+67}{3} \\ =\frac{194}{3} \\ =64.67 \end{gathered}$$

The weighted moving average is an indicator that is accustomed generates trade directions and making a buy or sells decision. It assigns greater weighting to recent data and less weighting to past data. The weighted moving average is calculated by multiplying each observation within the information set by a predetermined weighting factor.

$\text{Weighted moving average}=\left({\text{A}}_1*{\text{W}}_1+{\text{A}}_2*{\text{W}}_{2+}.........+{\text{A}}_{\text{n}}*{\text{W}}_{\text{n}}\right)$

The calculation of 3 months weighted moving average is done in column 5 by using the above formula:

Single exponential smoothing is the most employed in all forecasting techniques. It鈥檚 an integral part of all electronic forecasting programs, and it's widely employed in ordering inventory in retail businesses, manufacturing units, and repair agencies.

The calculation of the forecast for the period 2-12 is done in the table shown below by using the following formula:

${\text{F}}_{t+1}={\text{F}}_t+伪\left({\text{A}}_t-{\text{F}}_t\right),伪=.30$

Exponential smoothing with the trend is a forecasting method of time-series for univariate data which is further extended to support the data with the trend or seasonal constituent. It is the most powerful forecasting technique of forecasting.

The exponential smoothing with the trend is calculated by using the following formula in the table shown below:

${\text{F}}_{\text{t}}={\text{F}}_{\text{t}-1}+伪({\mathrm{A}}_{\text{t}-1}-{\text{F}}_{\text{t}-1})$

The mean absolute deviation is defined as the average distance between each data point and therefore the mean. It gives us a plan for the variability in an exceeding data set. Mean鈥� refers to the average of the observations and deviation implies departure or variation from a preset standard. When putting together, we can define deviation because of the distance of every observation from the mean of the information.

$\text{MAD}=\frac{\text{Sum of absolute value of each deviation}}{\text{Number of deviations}}$

By using the above formula the calculation of MAD is done as shown in the table below:

On the basis of MAD, the exponential smoothing with a trend component appears to be the best method. This should not be a surprise given the apparent upward trend.