# Beginners guide to ARIMA: ARIMA Forecasting technique learn by example

Word “ARIMA” in Tamil language the means Lion.

Everybody is curious and anxious enough to know what the future holds? It’s always exciting to know about it. Though there are various forecasting models available in this post we will look at ARIMA. Welcome to the world of Forecasting with ARIMA.

## What is ARIMA?

ARIMA is a forecasting technique. ARIMA– Auto Regressive Integrated Moving Average the key tool in Time Series Analysis. This link from Penn State University gives good introduction on the time series fundamentals.

## What is the purpose?

To Forecast. The book Forecasting: principles and practice gives a very good understanding to the whole subject. You can read it online.

## What kind of business problems it can solve?

To give examples the following are some of the use cases of ARIMA.

• Forecast revenue
• Forecast whether to buy a new asset or not
• Forecast of currency exchange rates
• Forecast consumption of energy or utilities

## What is mandate to get started?

1. It is very important to have clarity on what to forecast. Example if you want to forecast revenue whether it is for a product line, demography, etc., has to be analysed before venturing on to the actual task.
2. Period or the horizon in which the forecast is to be done is also crucial. Example: Monthly, Quarterly, Half-yearly etc.,

## What are the preferred pre-requisites on data for Time series forecasting?

### Updated after comment from tomdireill:

1. Data should be part of time series. That is data which is observed sequentially over time.
2. It can be seasonal. Means it should have highs and lows. As per the notes from Duke University it can be also applied on flat pattern less data too.
3. It should have trend of increasing or decreasing
4. outliers
can be handled as outlined here http://www.unc.edu/~jbhill/tsay.pdf

Ok, Now we got to understand what is essential to get started on forecasting, before we devolve lets work on the steps.

5 Steps towards forecasting:

In the next post we will take up an example and work on the above steps one by one. Keep waiting.