In this tutorial video, we outline the steps to conduct a periodogram analysis using our NumXL 1.64 (Turret).
Hello and welcome to the NumXL tutorial for Periodogram Analysis. In this video we will demonstrate the steps to construct a periodogram plot and identify key frequencies, if any embedded in the time series. For this tutorial we will use the monthly residual power demand of the city of Jeddah in the Kingdom of Saudi Arabia KSA between 2003 and 2010. We'll do the whole tutorial using only NumXL 1.64 release name turret and Microsoft Excel functionality.
First we took the natural logarithm of the monthly power demand to stabilize the variance. So in this tutorial whenever we mentioned demand we are referring to the logarithm value of the demand.
To start select an empty cell in your worksheet where you wish to place the output of the periodogram. Using the NumXL toolbar locate the periodogram icon and click on it.
The periodogram wizard pops up. By default the output range references the current selected cell in your worksheet. Now let's select the input data range in column D. Once the input data is selected the rest of the wizard options are now enabled and available. To continue click on the option tab.
Before we conduct the periodogram analysis we need to remove any deterministic trend or stochastic drift aka random walk. Under the data treatment we can manually choose the method if we need or leave it at auto to make it data-driven. For the auto data treatment the periodogram function uses the augmented dickey-fuller test aka ADF to test internally for stationarity and if needed best treatment, in other words difference or detrend.
Next, by default the wizard generates a plot for the periodogram and displays the first 42 half size of the input dataset frequency components. Let's leave all the settings unchanged. Click on the missing values tab.
The settings and the missing values tab are very similar to the missing value settings of other wizards in NumXL. In essence, you can instruct NumXL how to handle missing values if any are found. By default the "Don't Accept Missing Values" option is specified. Since our data set does not have any missing values we will leave the default option unchanged, click OK for now.
The periodogram table and plot are generated in your worksheet. In the periodogram plot one can easily observe a strong spike at point seven and to a smaller extent another spike at point 14. Let's mark those two data points clearly in our plot by enlarging their corresponding markers and changing their background colors.
In conclusion, the periodogram plot shows that the residual monthly demand has a strong seasonality with a length of 7 months. This is very dissimilar from our common belief in a 12 month seasonality. This finding can be attributed to the specific climate of Saudi Arabia.
That's it for now, thank you for watching!