What are the advantages and disadvantages of differencing for time series analysis?

2 When to use differencing

You should use differencing when your time series exhibits a non-stationary pattern that cannot be easily removed by other methods, such as detrending or deseasonalizing. For example, if your time series has an exponential growth or decay, or a multiplicative seasonality, you may need to difference it to make it stationary. You can check the stationarity of your time series by using tests such as the Augmented Dickey-Fuller (ADF) test or the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test. If the test rejects the null hypothesis of non-stationarity, you can conclude that your time series is stationary after differencing.

3 Advantages of differencing

Differencing has several advantages for time series analysis. First, it can simplify the modeling and forecasting process by reducing the complexity and variability of the time series. Second, it can improve the performance and accuracy of some models, such as autoregressive integrated moving average (ARIMA) models, that require stationarity as an assumption. Third, it can help identify the underlying structure and dynamics of the time series, such as the order of integration, the degree of autocorrelation, and the presence of shocks or outliers.

4 Disadvantages of differencing

Differencing also has some disadvantages for time series analysis. First, it can introduce noise and randomness into the time series, which can obscure the signal and reduce the information content. Second, it can affect the interpretation and meaning of the time series, as the differenced values may not have a clear or intuitive explanation. Third, it can cause problems with inference and hypothesis testing, as the differenced values may not follow a normal distribution, have constant variance, or be independent.

6 How to difference in Python

You can use Python to perform differencing on your time series data. One of the easiest ways is to use the pandas library, which provides a built-in method called diff() for differencing. You can specify the order of differencing by passing a parameter called periods, which defaults to 1. For example, if you have a pandas Series object called sales, you can difference it by using the following code:

sales_diff = sales.diff(periods=1)        

You can also use the numpy library, which provides a function called diff() for differencing. You can specify the order of differencing by passing a parameter called n, which defaults to 1. For example, if you have a numpy array object called sales, you can difference it by using the following code:

sales_diff = np.diff(sales, n=1)        

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?