The document outlines various methods and principles of forecasting, highlighting both judgmental and machine-generated forecasts. It covers techniques such as time series decomposition, exponential smoothing, ARIMA models, and the Delphi method, emphasizing the importance of a systematic approach to forecasting. Additionally, it discusses model evaluation and comparisons of forecasting accuracy.

1. Learnings from
Forecasting - Principles and Practice

2. www.otexts.org/fpp

3. Basic Modeling
Intro &
simple theory
Forecasting
Forecast+Model
www.otexts.org/fpp

4. Code at:
https://github.com/thiakx/Forecasting_DSSG
Adapted from the Forecasting: Principles and Practice book

6. Judgmental Forecasts Machine Generated Forecasts

7. Judgmental Forecasts
The Basics

8. Judgmental Forecasts - Principles
Set the forecasting task
clearly and concisely
Implement a systematic approach:
-Document and justify
-Systematically evaluate forecasts
Segregate forecasters and users

9. Judgmental Forecasts - How to
Delphi Method -
Panel of experts
Ask the executives, staff,
customers
Use a proxy
(similar cases, best / worst case)

11. Nate Silver

15. Machine Generated Forecasts
The Basics

16. The Basics -Time Series Decomposition

17. 12 month
seasonal
trend
The Basics - Seasonality

18. The Basics - Seasonality +Trend

19. The Basics - Remainder / Random Error

20. The Basics - Seasonal sub-series plot

21. The Basics

22. The Basics -Time Series Decomposition
then re-seasonalize with thisTrain model with this

23. The Basics -Time Series Decomposition
then re-seasonalize with thisTrain model with this

24. The Basics -Time Series Decomposition
95% confidence
80% confidence
then re-seasonalize with thisTrain model with this

25. The Basics -Time Series Decomposition
then re-seasonalize with thisTrain model with this

26. Seasonal andTrend decomposition using Loess (STL) code

27. Córdoba
Using seasonal-trend decomposition based on loess (STL) to
explore temporal patterns of pneumonic lesions in finishing
pigs slaughtered in England, 2005–2011

28. Córdoba
Using seasonal-trend decomposition based on loess (STL) to
explore temporal patterns of pneumonic lesions in finishing
pigs slaughtered in England, 2005–2011
STL is suitable as the overall trend fluctuates a fair bit

29. Machine Generated Forecasts
Time Series Forecasting:
Exponential smoothing

30. Time Series Forecasts - Holts-Winters

31. Time Series Forecasts - Exponential Smoothing

32. Time Series Forecasts - Exponential Smoothing
More relevantLess relevant
Something to represent fall in relevance?

33. Time Series Forecasts - Exponential Smoothing
Smoothing Parameter α = 0.8
0.8
0.16
0.032
0.064
T1
T2
T3
T4
T1-T4

34. Time Series Forecasts - Exponential Smoothing
T1-T4
Smoothing Parameter α = decided by minimizing error rate

35. Time Series Forecasts - Damping
Undamped projection
Damped projection

36. Time Series Forecasts - Additive vs Multiplicative
The additive method is
preferred when the
seasonal variations are
roughly constant
through the series,
while the multiplicative
method is preferred
when the seasonal
variations are changing
proportional to the
level of the series.

37. Time Series Forecasts - Holts-Winters

38. Link to: Usage of Modified Holt-Winters Method in the
Anomaly Detection of NetworkTraffic: Case Studies

39. Link to: Usage of Modified Holt-Winters Method in the
Anomaly Detection of NetworkTraffic: Case Studies
Holt-Winter is suitable as the most recent behavior that deviates from
norm is worth a lot more than past behavior

40. Machine Generated Forecasts
Time Series Forecasting:
ARIMA Models
(AutoRegressive Integrated Moving Average)

41. Time Series Forecasts - ARIMA with Drift
ARIMA(3,1,1)(0,1,1)[12] with drift

42. Time Series Forecasts - ARIMA with Drift
ARIMA(3,1,1)(0,1,1)[12] with drift
Allow forecasts to
change over time
Number of periods
per season.
}
}
Non-
Seasonal
Part
Seasonal
Part

43. Time Series Forecasts - ARIMA with Drift
ARIMA(3,1,1)(0,1,1)[12] with drift
Allow forecasts to
change over time
Number of periods
per season.
p = order of the autoregressive part;
d = degree of first differencing involved;
q = order of the moving average part.
}
}
Non-
Seasonal
Part
Seasonal
Part
}
(p,d,q)
}
(p,d,q)

44. Time Series Forecasts - Auto Regression
In a multiple regression model, we forecast the variable of interest
using a linear combination of predictors.
!
vs
!
In an autoregression model, we forecast the variable of interest using
a linear combination of past values of the variable
(regression of the variable against itself)

45. Time Series Forecasts - Auto Regression
In a multiple regression model, we forecast the variable of interest
using a linear combination of predictors.
!
vs
!
In an autoregression model, we forecast the variable of interest using
a linear combination of past values of the variable
(regression of the variable against itself)
Order = no. of past values

46. Time Series Forecasts - Differencing
What we doing in (b) is differencing by computing the differences
between consecutive observations.
The goal is to eliminate trend and seasonality.
(a) Dow Jones index (b) Daily change in Dow Jones index

47. Time Series Forecasts - Differencing
What we doing in (b) is differencing by computing the differences
between consecutive observations.
(a) Dow Jones index (b) Daily change in Dow Jones index
Order = no. of difference needed

48. Time Series Forecasts - Moving Average
Rather than use past values of the forecast variable in a regression, a
moving average model uses past forecast errors in a regression-like
model (a weighted moving average of the past few forecast errors).

49. Time Series Forecasts - Moving Average
Rather than use past values of the forecast variable in a regression, a
moving average model uses past forecast errors in a regression-like
model (a weighted moving average of the past few forecast errors).
Order = no. of past values

50. Time Series Forecasts - ARIMA with Drift
ARIMA(3,1,1)(0,1,1)[12] with drift

51. Link to Seasonal ARIMA for
Forecasting Air Pollution Index:
A Case Study (Johor Malaysia)

52. Link to Seasonal ARIMA for
Forecasting Air Pollution Index:
A Case Study (Johor Malaysia)
ARIMA is one of the most popular time series forecasting methods.
It is very flexible and can handle complex scenarios

53. Time Series Forecasts - Comparison of Accuracy

54. Time Series Forecasts - Comparison of Accuracy

56. Kudos to the awesome designers on thenounproject.com
Folder by Christina W
Checklist by João Marcelo Ribeiro
Fence by José Hernandez
Robot by Simon Child
Conference by Wilson Joseph
Meeting by Olivier Guin
Employee Evaluation by Miroslav Koša
People by iconoci
STL
ARIMA
Holt-Winters