This article provides a brief explanation of the ARIMA method of analytical forecasting.
What is ARIMA Forecasting?
Autoregressive Integrated Moving Average (ARIMA) predicts future values of a time series using a linear combination of its past values and a series of errors. This analytical forecasting method is suitable for instances when data is stationary/non stationary and is univariate, with any type of data pattern, i.e., level/trend/seasonality/cyclicity.
For more information about data trend and pattern analysis techniques, read our article entitled, ‘ What Are Data Trends and Patterns, and How Do They Impact Business Decisions?’
The ARIMA model is suggested for short term forecasting. ARIMA is only for univariate data forecasts, but there might be other variables affecting the output/dependent variable. ARIMA doesn’t take into account the influence of other predictors while forecasting, hence forecasts made might not be accurate.
Let’s look at an example of a monthly analysis of monthly index values. The plot of this data suggests that this is non-stationary data and that it shows a gradual upward trend (see the figure below). The ARIMA algorithm would be a suitable method for forecasting analysis because the data exhibits non-stationarity, and trend.
The ARIMA forecasting technique uses three primary parameters for analysis within the model.
p: to apply autoregressive model on series
d: to apply differencing on series. It converts non-stationary data to stationary to allow for a fairly constant level over time
q: to apply moving average model on series
How Can the ARIMA Forecasting Method Be Used for Enterprise Analysis?
In order to further examine the ARIMA forecasting method, and its application within an organization, let’s look at a sample use case.
Business Problem: A pharmaceutical company wants to predict the sales of a drug for the next two months, based on drug sales data from the past 12 months.
Data Pattern: Input data exhibits non-stationarity and cyclical pattern.
Business Benefit: The business can make use of these forecasts for better planning of drug production and accuracy of sales targets. This analysis also helps to balance supply and demand for the drug.
The ARIMA forecasting method is suitable for forecasting when data is stationary or non-stationary and is univariate with any type of data pattern. It will produce accurate, dependable forecasts, when planning for short-term business results. ARIMA provides forecasted values of the target variables for user-specified periods to clearly illustrate results for planning, production, sales and other factors.
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