CMA Training Video | Planning, Budgeting & Forecasting | FAR | By Varun Jain
Miles Education - CPA, CMA, CFA
Time series analysis is a type of regression analysis that considers time as the independent
variable. The dependent variable, in this case, is sales, which is represented by dollar amounts
on the y-axis of the graph. Long-term trends in sales are typically caused by factors such as
changes in technology, shifts in consumer preferences, or population changes. When asked to
forecast sales for quarter 17, smoothing methods can be used. One such method for smoothing
out the random fluctuations in the time series is called trend line smoothing, which involves
plotting a line on the graph that slopes upwards. The highlight moving average smoothing
method involves taking a simple average of the sales numbers, such as 166 dollars, plus 160
dollars, plus 80 dollars, and dividing that sum total by 3, which yields a sales projection of
168.67 dollars for quarter 17. Alternatively, the weighted average method for smoothing can be
used. This involves applying weights based on the underlying data table.
When analyzing data for quarter 16 and quarter 17, we also take into account the forecast for
quarter 16. To do this, we start at the end of quarter 15 and use a number (represented by "a")
for our calculations.If a = 0.7, then the formula for quarter 16 would be:0.7 x 180 (actual for
quarter 16) + (1 - 0.7) x (previous forecast) = forecast for quarter 16We can use the mnemonic
"1 minus a and circle f" to remember the formula for exponential smoothing.For example, let's
say we have a table of sales for quarters 14, 15, and 16. To forecast sales for quarter 17, we
can use the formula and plug in the relevant numbers, such as 0.2 for "a".
Miles Education - CPA, CMA, CFA
Time series analysis is a type of regression analysis that considers time as the independent
variable. The dependent variable, in this case, is sales, which is represented by dollar amounts
on the y-axis of the graph. Long-term trends in sales are typically caused by factors such as
changes in technology, shifts in consumer preferences, or population changes. When asked to
forecast sales for quarter 17, smoothing methods can be used. One such method for smoothing
out the random fluctuations in the time series is called trend line smoothing, which involves
plotting a line on the graph that slopes upwards. The highlight moving average smoothing
method involves taking a simple average of the sales numbers, such as 166 dollars, plus 160
dollars, plus 80 dollars, and dividing that sum total by 3, which yields a sales projection of
168.67 dollars for quarter 17. Alternatively, the weighted average method for smoothing can be
used. This involves applying weights based on the underlying data table.
When analyzing data for quarter 16 and quarter 17, we also take into account the forecast for
quarter 16. To do this, we start at the end of quarter 15 and use a number (represented by "a")
for our calculations.If a = 0.7, then the formula for quarter 16 would be:0.7 x 180 (actual for
quarter 16) + (1 - 0.7) x (previous forecast) = forecast for quarter 16We can use the mnemonic
"1 minus a and circle f" to remember the formula for exponential smoothing.For example, let's
say we have a table of sales for quarters 14, 15, and 16. To forecast sales for quarter 17, we
can use the formula and plug in the relevant numbers, such as 0.2 for "a".
