Skip to content Skip to navigation


You are here: Home » Content » Discrete-Time Filtering Example


Recently Viewed

This feature requires Javascript to be enabled.

Discrete-Time Filtering Example

Module by: Don Johnson. E-mail the author

Summary: Average stock price as an example of discrete-time filtering.

Example 1

Suppose we want to average daily stock prices taken over last year to yield a running weekly average (average over five trading sessions). The filter we want is a length-5 averager (as shown in the unit-sample response), and the input's duration is 253 (365 calendar days minus weekend days and holidays). The output duration will be 253+51=257 253 5 1 257 , and this determines the transform length we need to use. Because we want to use the FFT, we are restricted to power-of-two transform lengths. We need to choose any FFT length that exceeds the required DFT length. As it turns out, 256 is a power of two ( 28=256 2 8 256 ), and this length just undershoots our required length. To use frequency domain techniques, we must use length-512 fast Fourier transforms.

Figure 1: The blue line shows the Dow Jones Industrial Average from 1997, and the red one the length-5 boxcar-filtered result that provides a running weekly of this market index. Note the "edge" effects in the filtered output.
Figure 1 (sig23.png)

Figure 1 shows the input and the filtered output. The MATLAB programs that compute the filtered output in the time and frequency domains are

	Time Domain 
	h = [1 1 1 1 1]/5; 
	y = filter(h,1,[djia zeros(1,4)]);

	Frequency Domain
	h = [1 1 1 1 1]/5; 
	DJIA = fft(djia, 512);
	H = fft(h, 512);
	Y = H.*X;
	y = ifft(Y);


The filter program has the "feature" that the length of its output equals the length of its input. To force it to produce a signal having the proper length, the program zero-pads the input appropriately.
MATLAB's fft function automatically zero-pads its input if the specified transform length (its second argument) exceeds the signal's length. The frequency domain result will have a small imaginary component — largest value is 2.2×10-11 2.2-11 — because of the inherent finite precision nature of computer arithmetic. Because of the unfortunate misfit between signal lengths and favored FFT lengths, the number of arithmetic operations in the time-domain implementation is far less than those required by the frequency domain version: 514 versus 62,271. If the input signal had been one sample shorter, the frequency-domain computations would have been more than a factor of two less (28,696), but far more than in the time-domain implementation.

An interesting signal processing aspect of this example is demonstrated at the beginning and end of the output. The ramping up and down that occurs can be traced to assuming the input is zero before it begins and after it ends. The filter "sees" these initial and final values as the difference equation passes over the input. These artifacts can be handled in two ways: we can just ignore the edge effects or the data from previous and succeeding years' last and first week, respectively, can be placed at the ends.

Content actions

Download module as:

PDF | EPUB (?)

What is an EPUB file?

EPUB is an electronic book format that can be read on a variety of mobile devices.

Downloading to a reading device

For detailed instructions on how to download this content's EPUB to your specific device, click the "(?)" link.

| More downloads ...

Add module to:

My Favorites (?)

'My Favorites' is a special kind of lens which you can use to bookmark modules and collections. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need an account to use 'My Favorites'.

| A lens I own (?)

Definition of a lens


A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

| External bookmarks