Quantitative modelling
2 exams, but one resit
2 online BONUS Quiz (December 6, January 17)
- Bonus quiz 1: 3 multiple choice, each 0,05
- Bonus quiz 2: 3 multiple choice, each 0,05
Exam 2 is harder
Week 1:
Chapter 15:
Forecasting = prediction of what will occur in the future based on historical data.
Everything starts with right forecasting.
Forecasts are always wrong!!
2 forcasting components:
- Time frames
Short-range (day, week, one to two months)
Medium-range (two months to one year)
Long range (more than one or two years)
- Patterns: trend, random variations, cycles, seasonal pattern
trend = the long-term movement of the item being forecasted (up-or-down
movement of demand)
random variations = movements that are not predictable and don’t follow a pattern
cycle = a movement up or down , that repeats itself over a lengthy time span
seasonal pattern = oscillating movement in demand that occurs periodically in a short
run and repetitive
The farther to predict the future, the harder the forecast
The frame can change from one company to another
,In WMA you don’t have to divide the periods!!!!!!!
F1 = always D1 in simple exponential smoothing
Linear trend line
Formula y = a + bx
a = intercept (at period 0)
b = slope of the line
x = the time period
y = forecast for demand for period x
bar above x and y = mean
linear trend line= straight line upwards
Seasonal adjustments
- a seasonal pattern = repetitive up-and-down movement in demand
- seasonal patterns can occur on a quarterly, monthly, weekly, or daily basis
- seasonally adjusted forecast = can be developed by multiplying the normal forecast
by a seasonal factor
- Seasonal factor = can be determined by dividing the actual demand for each seasonal
period by total annual demand
Seasonal factor =
- lie between 0 and 1
- Represent portion of total annual demand assigned to each season
, - Seasonal factors are multiplied by annual demand to provide adjusted forecasts for
each period
D = demand
Forecast error = E = D – F
D = actual demand
F = forecast
If outcome is large:
- Technique is wrong
- Or parameters need adjusting
2 exams, but one resit
2 online BONUS Quiz (December 6, January 17)
- Bonus quiz 1: 3 multiple choice, each 0,05
- Bonus quiz 2: 3 multiple choice, each 0,05
Exam 2 is harder
Week 1:
Chapter 15:
Forecasting = prediction of what will occur in the future based on historical data.
Everything starts with right forecasting.
Forecasts are always wrong!!
2 forcasting components:
- Time frames
Short-range (day, week, one to two months)
Medium-range (two months to one year)
Long range (more than one or two years)
- Patterns: trend, random variations, cycles, seasonal pattern
trend = the long-term movement of the item being forecasted (up-or-down
movement of demand)
random variations = movements that are not predictable and don’t follow a pattern
cycle = a movement up or down , that repeats itself over a lengthy time span
seasonal pattern = oscillating movement in demand that occurs periodically in a short
run and repetitive
The farther to predict the future, the harder the forecast
The frame can change from one company to another
,In WMA you don’t have to divide the periods!!!!!!!
F1 = always D1 in simple exponential smoothing
Linear trend line
Formula y = a + bx
a = intercept (at period 0)
b = slope of the line
x = the time period
y = forecast for demand for period x
bar above x and y = mean
linear trend line= straight line upwards
Seasonal adjustments
- a seasonal pattern = repetitive up-and-down movement in demand
- seasonal patterns can occur on a quarterly, monthly, weekly, or daily basis
- seasonally adjusted forecast = can be developed by multiplying the normal forecast
by a seasonal factor
- Seasonal factor = can be determined by dividing the actual demand for each seasonal
period by total annual demand
Seasonal factor =
- lie between 0 and 1
- Represent portion of total annual demand assigned to each season
, - Seasonal factors are multiplied by annual demand to provide adjusted forecasts for
each period
D = demand
Forecast error = E = D – F
D = actual demand
F = forecast
If outcome is large:
- Technique is wrong
- Or parameters need adjusting
