Types of prediction
The prediction type can be classified according to various criteria. Basic criteria are
- data that we have for teaching prediction and for prediction
- what we want to predict - value or trend
Predicting value or trend
When we want to get exact value (or more values) of a variable in future, then we are predicting value. Other possibility is to predict trend of a variable, i.e., whether the value will go up or down without considering size of the change - then we are predicting trend. When predicting trend we are in fact classifying into two (or three) classes - up and down (or no significant change). Prediction of a close value is generally easier than predicting a trend. Besides trend we may also want to predict other parts of the trend, such as moving average change etc.
Data for prediction
For time series predicting we usually have values of a variable in equidistant intervals - then we can try to predict the development of the value based on historical values and time only. In this case the historical time series should be long enough and dense enough.
We can have additional information to time series, such as derivation. This information can be then used for more exact prediction. Important information can be added using so called interventional variables (intervention indicators), which represent information about time series or information about the period into which we predict. For example when predicting energy consumption then knowing whether we predict for Monday or Saturday can improve the prediction dramatically - this information does not follow from time series explicitly and must be added. It is usually very helpful to use the values of intervention indicator when creating a model that will be used for prediction.
We can also have information about other related variables, preferably also in time series. From the history of related variables we can reason about other variables. The relation can be expressed in various ways. An example is a static (or slowly changing) sum of two variables. It does not have to be expressed explicitly - for example changes of prices of stocks of shares in one sector are dependent, but can be hard to express in a computational way. This kind of information is selected in a hope that it will cohere with the predicted value, but we do not have to be sure about it. The field of data mining can help with selecting appropriate information and its interpretation.