What is differencing in time series and why do we do it in R

This recipe explains what is differencing in time series and why do we do it in R

Recipe Objective

What is differencing in time series and why do we do it?

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. A stationary time series is the one whose properties do not depend on the time at which the series is observed. The stationary time series data has a mean zero and constant variance. When the time series data has a trend/seasonality, the data is non-stationary in nature. If the non-stationary data is to be converted into a stationary data, the difference between consecutive observations is computed, which is known as 'differencing'. This recipe demonstrates an example of differencing in time series.

Apply Machine Learning to Demand Forecasting Data Science Problems

Step 1 - Install required package

install.packages('forecast') library(forecast)

Step 2 - Using the co2 dataset

data <- co2 head(data)

Step 3 - Plot the data

plot(data)

Step 4 - Use diff()

**Syntax - diff(data, lag, differences)** where, data - time series data with trend or seasonality lag - the lag at which to difference differences - the order of differencing

nw_ts<-diff(data,differences = 2) plot(nw_ts)

We defined the differences parameter as '2' i.e twice differencing in order to remove the trend from the time series data.

nw_ts2 <- diff(nw_ts,lag=12) plot(nw_ts2)

Defining the lag parameter as '12' helps remove the seasonality effect from the data. The nw_ts2 is now a stationary time series data with mean = 0 and a constant variance.

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