Forecasting Techniques Questions and Answers

Q: The time series forecasting method that gives equal weightage to each of the m most recent observations is

The method is the moving average method.A simple moving average forecasts the next period as the arithmetic mean of the last m observations, so each of those m values gets the same weight, 1/m, and older data get none.Exponential smoothing, in all its forms (with a linear trend, or triple smoothing for trend and seasonality), gives weights that decline geometrically with the age of the data, the most recent observation weighted most heavily. A Kalman filter is a recursive state estimator whose weighting (the Kalman gain) depends on the noise statistics, not an equal weighting.

Which of the following forecasting models gives more weight to recent data and is typically used when a trend or pattern exists in the data?

A.Exponential Smoothing
B.Simple Moving Average
C.Delphi Method
D.Weighted Moving Average

Exam Year(s)

DSSSB - 2023,

Answer: A

Exponential smoothing is the forecasting model that gives more weight to recent data, with weights falling exponentially for older data, and it is used when a trend or pattern is present. The simple moving average gives equal weights to the data of the period, and the Delphi method is a qualitative method that takes expert opinion. The weighted moving average also gives more weight to recent data, but the key names exponential smoothing as the model with the pattern.

Discuss in Forum

The time series forecasting method that gives equal weightage to each of the m most recent observations is

A.Moving average method
B.Exponential smoothing with linear trend
C.Triple Exponential smoothing
D.Kalman Filter

Exam Year(s)

GATE ME - 2018,

Answer: A

The method is the moving average method.

A simple moving average forecasts the next period as the arithmetic mean of the last m observations, so each of those m values gets the same weight, 1/m, and older data get none.

Exponential smoothing, in all its forms (with a linear trend, or triple smoothing for trend and seasonality), gives weights that decline geometrically with the age of the data, the most recent observation weighted most heavily. A Kalman filter is a recursive state estimator whose weighting (the Kalman gain) depends on the noise statistics, not an equal weighting.

Discuss in Forum

Considering the actual demand and the forecast for a product given in the table below, the mean forecast error and the mean absolute deviation, respectively, are

Period	1	2	3	4	5	6	7	8	9	10
Actual demand	425	415	420	430	427	418	422	416	426	421
Forecast	427	422	416	422	423	420	419	418	430	415

A.0.8 and 42.0
B.0.8 and 4.2
C.8.0 and 42.0
D.8.0 and 4.2

Exam Year(s)

GATE ME - 2025,

Answer: B

The mean forecast error is 0.8 and the mean absolute deviation is 4.2: 0.8 and 4.2.

The errors (actual minus forecast) for periods 1 to 10 are \(-2, -7, 4, 8, 4, -2, 3, -2, -4, 6\).

\[ \begin{aligned} \text{MFE} &= \tfrac{8}{10} = 0.8 \\ \text{MAD} &= \tfrac{2+7+4+8+4+2+3+2+4+6}{10} = \tfrac{42}{10} = 4.2 \end{aligned} \]

42.0 is the sum of absolute errors before dividing by 10, and 8.0 the sum of errors; the other options use these totals in place of the means.

Discuss in Forum

Loading…

Topics

Other Subjects

Tags: Forecasting Techniques Questions and Answers || Forecasting Techniques MCQ Questions and Answers || Forecasting Techniques GK Questions and Answers || Forecasting Techniques GK MCQ Questions || Forecasting Techniques Multiple Choice Questions and Answers || Forecasting Techniques GK || GK on Forecasting Techniques || Production Planning and Control Questions and Answers || Production Planning and Control MCQ Questions and Answers || Production Planning and Control GK Questions and Answers || GK on Production Planning and Control

Forecasting Techniques Questions and Answers

Topics

Other Subjects

About Us

Stay informed about all important Dates & Notifications