Time Series Forecasting is the process of analyzing and modeling time-series data. It helps in forecasting the future behaviour of the market, which is helpful in the decision-making for every business. Some of the applications of Time Series Forecasting are weather and climate forecasting, sales forecasting, business forecasting, stock market prediction, etc. While working on a time series forecasting problem, you should know how to choose a forecasting model. So, if you don’t know about choosing a forecasting model, this article is for you. In this article, I will take you through how to choose a Time Series Forecasting Model.
Here’s How to Choose a Time Series Forecasting Model
The choice of the Time Series Forecasting model depends on the type of time series data you are working with. There are four types of time series data:
- Stationary data
- Data with trends
- Seasonal data
- Cyclical series data
Let’s go through all these types of time series data and understand how to choose a time-series forecasting model for each data type.
When the Data is Stationary
Stationary data is data whose mean value doesn’t change in the long run. Let’s say the monthly sales of milk in a town. The consumers of milk will buy milk daily, and the vendors will also purchase milk according to the demand for milk in their area. It is an example of stationary data where the mean value doesn’t change with the change in time.
The Autoregressive Movie Average model (ARMA) is one of the best models you should choose while working on stationary data.
While Working with Data with Trends
The trends in data exist when there is a long-term increase or decrease in a dataset. The increase or decrease can be linear or nonlinear. In simple words, the changes in the patterns are not constant with the change in time. For example, the demand for electricity in a particular region, the cost of producing a product during economic changes, etc.
The Autoregressive Integrated Moving Average model (ARIMA) is one of the best Time Series Forecasting models you should choose while working on data with trends.
When Data is Seasonal
Seasonal data is data which is affected by seasonal factors. The seasonal factors can be a particular month of the year, a week of the month, or a day of the week. In simple words, when the patterns of a data repeat after a particular time, the data is seasonal. For example, getting a higher reach on Instagram every Sunday, high sales during the festive season, etc.
The Seasonal Autoregressive Integrated Moving Average model (SARIMA) is one of the best Time Series Forecasting models you should choose while working on seasonal data.
While Working with Cyclical Series Data
A cyclical series data is a data that contains increase and decrease trends with no fixed frequency. These patterns are due to market behaviour and the business environment. In simple words, if the data has fluctuations with no fixed frequency, it is cyclical. For example, perfect competition market, changes in fashion, natural disasters etc.
Cyclical series are difficult to forecast because the patterns are not stable. Forecasting time series data with a cyclical series requires finding the leading economic indicators.
The choice of the Time Series Forecasting model depends on the type of time series data you are working with. So when the data is stationary, choose the ARMA model. When the data has trends, choose the ARIMA model. When it is seasonal, choose the SARIMA model. And if the dataset contains a cyclical series, you need to find the leading economic indicators. I hope you liked this article on choosing a Time Series Forecasting model. Feel free to ask valuable questions in the comments section below.