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Basic terminologies of time series and forecasting can be difficult to understand. There are four basic learning points:
Forecasting is the prediction of future events and conditions and is a key element in service organizations, especially banks, for management decision-making. There are typically two types of events: 1) uncontrollable external events – originating with the national economy, governments, customers and competitors and 2) controllable internal events (e.g., marketing, legal, risk, new product decisions) within the firm.
The need for forecasting stems from the time lag between awareness of an impending event or need and the occurrence of that event. Organizations constantly try to predict economic events and their impact.
The following are a few applications for forecasting modules:
There are some basic steps for creating a forecast:
A forecasting system consists of two primary functions: forecast generation and forecast control. Forecast generation includes acquiring data to revise the forecasting model, producing a statistical forecast and presenting results to the user. Forecast control involves monitoring the forecasting process to detect out-of-control conditions and identifying opportunities to improve forecasting performance. Figure 1 shows a visualization of a forecasting system and process.
There are a number of common terms when discussing forecasting models:
In any forecasting, it is the accuracy – or “goodness of fit” – of future forecast(s) that is most important. There are three commonly used statistical measures used in forecasting: 1) mean absolute percentage error (MAPE), 2) mean absolute deviation error (MAD or MADeviation) and 3) predicted/mean squared deviation error (PMSE or MSDeviation).
If Yt is the observed value at period t and Ft is the forecasted value at period t
|Table 1: Examples of Forecast Error Measures|
|Period||Observed||Forecast||Abs Error||% Abs Error||Deviation2|
Adjustments by inflation: a cause of variation that affects time series is the effect of inflation.When forecasting the price of a new SUV (or car), it is essential to take into account the effect of inflation, as a $80,000 SUV this year is not the same as a $80,000 SUV 10 years ago. By using the equivalent value in the year 2007, for example, the data are then directly comparable and forecasts will have one less source of variation.
There are many factors to consider when choosing a forecast method. Two major considerations are cost versus risk.
“Prediction is very difficult, especially if it’s about the future.”
–Nils Bohr, Nobel laureate in Physics
This quote serves as a warning of the importance of testing a forecasting model out-of-sample. It is often easy to find a model that fits the past data well. It is quite another matter to find a model that correctly identifies those features of the past data that will be replicated in the future. Do not create a model to be an exact replica of reality. Create a model because it is quick and easy and guides toward reality. A model also emphasizes the complexity and unreliability of predictions.