Moving Averages For Forecasting Trends in Internet Data
Moving Averages (MAs) are a powerful technique used to see short- and long-term trends in any time-based data. For example, stock market day traders use MAs using a "window" of a specified number of days to produce a "sliding average" of a stock's price, or of a market index. The resulting MA graph shows a smoother curve than the daily graph. From the MA, and assuming you have a reasonable window of time, you can see how a stock has been behaving and make an educated guess as to how it'll perform. The flaw in this technique is that if something drastic and random happens, you cannot predict it.
The MA method can be used to analyze trends in any data. If you are a website selling some products online, or a blogsite owner running advertising in your blogs, you can an MA to gauge how sales/revenue has been, and how it MIGHT be in the near future.
The technique in a nutshell:
(1) Have a sufficient period of data. I don't like to use anything less than 1 year, but that doesn't mean you can't. For ease of explanation, let's assume that we are measuring daily visitors to your blog(s).
(2) Choose a sliding window. If you want to gauge short-term trends, choose a short window, say, 15 days. If you want to gauge long-term trends, choose a longer window (up to 365 days). Keep in mind that the larger the window, the more data you must have or else the MA is bogus.
(3) Starting with day 1, add up the first 15 days of data (or whatever window you are using). Divide the total by the window size, i.e., 15 days. This is the first MA value, and it is plotted on day 15.
(4) Now slide over to day 2, and total up the data from day 2 through day 16, inclusive (15 day window). Divide the total once again by 15 days. This the second MA value, and it is plotted on day 16.
(5) Repeat this process until the end of the window includes the last day that you have data for. For example, if you have 35 days of data, then the last day for which you will calculate an average on the 15-d window is (35-15)+1 = day 21 (not day 20).
(6) The resulting graph of averages is the MA graph.
Of course, analyzing on a single window is not as useful as using multiple windows. For example, in the stock market, you want to analyze both short-term and long-term trends simultaneously, especially if you want to be a successful daytrader. Using multiple windows is called Multiple Moving Averages (MMAs). An example MMA graph is shown below:
The above graph shows the MMAs for a daily electronic writing journal I was keeping for my fiction and non-fiction a few years ago. The general measure is the number of words that I wrote each day. You can see how the light blue line, which uses a 20-day sliding window, is smoother than the dark blue line (which is the original data). The interval I've used in the example is too short to give meaningful results, but the techniques are sound. You can use these techniques to analyze both sales and visitors, and whether there is a correspondence between them.
(c) Copyright 2005-present, Raj Kumar Dash, http://netmetrics.blogspot.com
The MA method can be used to analyze trends in any data. If you are a website selling some products online, or a blogsite owner running advertising in your blogs, you can an MA to gauge how sales/revenue has been, and how it MIGHT be in the near future.
The technique in a nutshell:
(1) Have a sufficient period of data. I don't like to use anything less than 1 year, but that doesn't mean you can't. For ease of explanation, let's assume that we are measuring daily visitors to your blog(s).
(2) Choose a sliding window. If you want to gauge short-term trends, choose a short window, say, 15 days. If you want to gauge long-term trends, choose a longer window (up to 365 days). Keep in mind that the larger the window, the more data you must have or else the MA is bogus.
(3) Starting with day 1, add up the first 15 days of data (or whatever window you are using). Divide the total by the window size, i.e., 15 days. This is the first MA value, and it is plotted on day 15.
(4) Now slide over to day 2, and total up the data from day 2 through day 16, inclusive (15 day window). Divide the total once again by 15 days. This the second MA value, and it is plotted on day 16.
(5) Repeat this process until the end of the window includes the last day that you have data for. For example, if you have 35 days of data, then the last day for which you will calculate an average on the 15-d window is (35-15)+1 = day 21 (not day 20).
(6) The resulting graph of averages is the MA graph.
Of course, analyzing on a single window is not as useful as using multiple windows. For example, in the stock market, you want to analyze both short-term and long-term trends simultaneously, especially if you want to be a successful daytrader. Using multiple windows is called Multiple Moving Averages (MMAs). An example MMA graph is shown below:
The above graph shows the MMAs for a daily electronic writing journal I was keeping for my fiction and non-fiction a few years ago. The general measure is the number of words that I wrote each day. You can see how the light blue line, which uses a 20-day sliding window, is smoother than the dark blue line (which is the original data). The interval I've used in the example is too short to give meaningful results, but the techniques are sound. You can use these techniques to analyze both sales and visitors, and whether there is a correspondence between them.
(c) Copyright 2005-present, Raj Kumar Dash, http://netmetrics.blogspot.com
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