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 demand for the next period on the assumption that the past will repeat and any trends, seasonality, or cycles are either reflected in the previous period's demand or do not exist.
Simple average - takes the average of some number of periods of past data by summing each period and dividing the result by the number of periods. (great for short term basic forecasting)
Moving average takes a predetermined number of periods, sums their actual demand, and divides by the number of periods to reach a forecast. For each subsequent period, the oldest period of data drops off and the latest period is added
Weighted average applies a predetermined weight to each month of past data, sums the past data from each period then divides by the total of the weights. If the forecaster adjusts the weights so that their sum is equal to 1, then the weights are multiplied by the actual demand of each applicable period. The results are then summed to achieve a weighted forecast. Generally, the more recent the data is, the higher the weight.
Weighted moving average this is a combination of weighted and moving average which assigns weights to a predetermined number of periods of actual data and computes the forecast the same way as moving average forecasts. As with all moving forecasts, as each new period is added, the data from the oldest period is discarded.
Exponential smoothing is a more complex form of weighted moving average in which the weight falls off exponentially as the data ages. This averaging technique takes the previous period's forecast and adjusts it by a predetermined smoothing constant multiplied by the difference in the previous forecast and the demand that actually occurred during the previously forecasted period (called forecast error).
Holt's Model An extension of exponential smoothing used when time-series data exhibits a linear trend. This method is known by several other names: double smoothing; trend-adjusted exponential...
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