Associative and Time Series Forecasting Models
Forecasting Models: Associative and Time Series
Forecasting involves using past data to generate a number, set of numbers, or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning.
Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research.
Time Series Models
Based on the assumption that history will repeat itself, there will be identifiable patterns of behaviour that can be used to predict future behaviour. This model is useful when you have a short time requirement (eg days) to analyse products in their growth stages to predict short-term outcomes.
To use this model you look at several historical periods and choose a method that minimises a chosen measure of error. Then use that method to predict the future. To do this you use detailed data by SKU's (Stock Keeping Units) which are readily available.
In TSM there may be identifiable underlying behaviours to identify as well as the causes of that behaviour. The data may show causal patterns that appear to repeat themselves – the trick is to determine which are true patterns that can be used for analysis and which are merely random variations. The patterns you look for include:
Trends – long term movements in either direction
Cycles - wavelike variations lasting more than a year usually tied to economic or political conditions (eg gas prices have long term impact on travel trends)
Seasonality – short-term variations related to season, month, particular day (eg Christmas sales, Monday trade etc)
In addition there are causes of behaviour that are not patterns such as worker strikes, natural disasters and other random variations.
Simple uses of this model include “naive” forecasting & averaging but both take little account of the variations and patterns.
“Naive” forecast uses the actual demand for the past period as the forecasted
Forecasting involves using past data to generate a number, set of numbers, or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning.
Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research.
Time Series Models
Based on the assumption that history will repeat itself, there will be identifiable patterns of behaviour that can be used to predict future behaviour. This model is useful when you have a short time requirement (eg days) to analyse products in their growth stages to predict short-term outcomes.
To use this model you look at several historical periods and choose a method that minimises a chosen measure of error. Then use that method to predict the future. To do this you use detailed data by SKU's (Stock Keeping Units) which are readily available.
In TSM there may be identifiable underlying behaviours to identify as well as the causes of that behaviour. The data may show causal patterns that appear to repeat themselves – the trick is to determine which are true patterns that can be used for analysis and which are merely random variations. The patterns you look for include:
Trends – long term movements in either direction
Cycles - wavelike variations lasting more than a year usually tied to economic or political conditions (eg gas prices have long term impact on travel trends)
Seasonality – short-term variations related to season, month, particular day (eg Christmas sales, Monday trade etc)
In addition there are causes of behaviour that are not patterns such as worker strikes, natural disasters and other random variations.
Simple uses of this model include “naive” forecasting & averaging but both take little account of the variations and patterns.
“Naive” forecast uses the actual demand for the past period as the forecasted