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These worksheets are great for students to revise Drawing Inequality Graphs. Given a series of numbers and a fixed subset size, the first element of the moving average is obtained by taking the average of the initial fixed subset of the number series. The threshold between short-term and long-term depends on the application, and the parameters of the moving average will be set accordingly. When used with non-time series data, a moving average filters higher frequency components without any specific connection to time, although typically some kind of ordering is implied. Viewed simplistically it can be regarded as smoothing the data.
Moving Average Types comparison – Simple and Exponential. However, in science and engineering the mean is normally taken from an equal number of data on either side of a central value. This ensures that variations in the mean are aligned with the variations in the data rather than being shifted in time. The period selected depends on the type of movement of interest, such as short, intermediate, or long-term. If the data used are not centered around the mean, a simple moving average lags behind the latest datum point by half the sample width. An SMA can also be disproportionately influenced by old datum points dropping out or new data coming in.
But a perfectly regular cycle is rarely encountered. For a number of applications, it is advantageous to avoid the shifting induced by using only ‘past’ data. This requires using an odd number of datum points in the sample window. A major drawback of the SMA is that it lets through a significant amount of the signal shorter than the window length.
This can lead to unexpected artifacts, such as peaks in the smoothed result appearing where there were troughs in the data. It also leads to the result being less smooth than expected since some of the higher frequencies are not properly removed. For example, an investor may want the average price of all of the stock transactions for a particular stock up until the current time. The brute-force method to calculate this would be to store all of the data and calculate the sum and divide by the number of datum points every time a new datum point arrived.
It is also possible to store a running total of the datum point as well as the number of points and dividing the total by the number of datum points to get the CMA each time a new datum point arrives. The derivation of the cumulative average formula is straightforward. A weighted average is an average that has multiplying factors to give different weights to data at different positions in the sample window. In the more general case the denominator will always be the sum of the individual weights. The graph at the right shows how the weights decrease, from highest weight for the most recent datum points, down to zero.
It can be compared to the weights in the exponential moving average which follows. The graph at right shows an example of the weight decrease. This is sometimes called a ‘spin-up’ interval. One way to assess when it can be regarded as reliable is to consider the required accuracy of the result. Sometimes with very small alpha, this can mean little of the result is useful.
The above discussion requires a bit of clarification. Both of these sums can be derived by using the formula for the sum of a geometric series. Again, it is just a recommendation—not a requirement. The question of how far back to go for an initial value depends, in the worst case, on the data. Large price values in old data will affect the total even if their weighting is very small. If prices have small variations then just the weighting can be considered.
For example, to have 99. EWMVar can be computed easily along with the moving average. Some computer performance metrics, e. CPU utilization, use a form of exponential moving average.
Outside the world of finance, weighted running means have many forms and applications. Each weighting function or “kernel” has its own characteristics. In engineering and science the frequency and phase response of the filter is often of primary importance in understanding the desired and undesired distortions that a particular filter will apply to the data. A mean does not just “smooth” the data. A mean is a form of low-pass filter. The effects of the particular filter used should be understood in order to make an appropriate choice. From a statistical point of view, the moving average, when used to estimate the underlying trend in a time series, is susceptible to rare events such as rapid shocks or other anomalies.