Associative and Time Series Forecasting Models

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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
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This can be analysed using either the multiplicative or additive method. In the additive version, seasonality is expressed as a quantity to be added to or subtracted from the series average. For the multiplicative model seasonality is expressed as a percentage (seasonal relatives or seasonal indexes) of the average (or trend). These are then multiplied times values in order to incorporate seasonality.

Associative Models

Also known as “causal” models involve the identification of variables that can be used to predict another variable of interest. They are based on the assumption that the historical relationship between "dependent" and"independent" variables will remain valid in future and each independent variable is easy to predict. This form of analysis can take several months and is used for medium-term forecasts for products in their growth or maturity phase.

The procedure for this model is to collect several periods of history relating to the independent and dependent variables themselves, establish the relationship that minimizes mean squared error of forecast vs actual using linear or non-linear and singular or multiple regression analysis.

So you first predict the independent variable, then look at the established relationships between that independent variable and the dependent ones to predict what the dependent variables will be. You then develop an equation that summarizes the effects of predictor variables.

To do this you will need aggregate data which

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