In general, traditional algorithms tend to use predefined techniques and statistical models such as linear regression, autoregressive integrated moving average (ARIMA), and autoregressive integrated moving average with explanatory variable (ARIMAX). The goal of traditional forecasting methods is largely descriptive in nature, aimed at analyzing a univariate dataset or a multivariate dataset with finite, countable, and explainable predictors.
The objective of a forecast model is to estimate future value – usually from historical records of business performance metrics. More specifically, forecasts also include a confidence interval that expresses the level of certainty in a given prediction. In many cases, business performance data might be univariate, meaning that the type of data consists of observations on only a single characteristic or variable.
For instance, in predicting the sales of a fast-moving consumer product – such as dairy products – traditional statistical methods might give a reasonable forecast accuracy based on historical data. This forecast occurs because the number of dimensions that might affect the sales of such products is finite and countable. Though a machine-learning algorithm forecasting sales might provide better accuracy, it will be at the cost of explainability and computing power.
Traditionally, some of the following classical models are used effectively when dealing with univariate data with a high degree of accuracy:
- Moving average
- Simple exponential smoothing (SES)
- Damped exponential smoothing (DES)
- Average of SES, Holt, and DES
- Linear regression
- ARIMA, ARIMAX
- Unobserved component modeling
One of the major features of these classical models is the level of transparency into how they function. The outputs provided by these models can be easily traced (figure 2).