How to decompose a time series in R

This recipe helps you decompose a time series in R

Recipe Objective

How to decompose a time series in R.

Time series is a statistical technique that deals with time series data or trend analysis. Time series data means the data is collected over a period of time/ intervals. Time series data helps us with making forecasting based on the previously collected data. There are 4 basic components of a time series: - Secular Trend / Trend - Level - Noise - Seasonality There are two types of time series models, additive which adds all the four components together and multiplicative model which multiplies all the four models together. Decomposition provides a useful abstract model for thinking about time series generally and for better understanding problems during time series analysis and forecasting. Decomposition is an abstract model used for better understanding of the data during time series analysis and forecasting. This recipe demonstrates an example of decomposition of a time series in R.

Learn About the Application of ARCH and GARCH models in Real-World

Step 1 - Use air passenger dataset

data = AirPassengers
plot(data, main = "Time series data")
summary(data)

Step 2 - Decompose the data

plot(decompose(data))
plot(decompose(data, type = "multiplicative"))

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