Presentation by Kai Xin on techniques learnt from Forecasting - Principles and Practice book: www.otexts.org/fpp
Cover techniques like Seasonal and Trend decomposition using Loess (STL), Holts-Winters, ARIMA etc. R code adapted from the book is available at:
https://github.com/thiakx/Forecasting_DSSG
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)
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
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.
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
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
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
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