MESA Best-Fit Prediction

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The MESA Best Fit study is a cycle prediction based on a sine wave cycle that best fits data over cycle lengths from 6 to 50 bars.  The MESA Best-Fit Prediction study answers the question, "Among the sine-wave cycles that have wavelengths in the user-specifiable range, which one best fits the price data over its own wavelength, and what would that cycle predict over the next 10?"

 

 

It is a more customizable study that makes it easier to identify any short-wavelength cycles as they develop.  Cycles that do not extend back close to 30 bars have not lasted long enough to be identified as the dominant cycle of the standard prediction, but other evidence sometimes indicates that they may exist.  The study searches for cycles of wavelengths in a user-specifiable range that is usually set at the default of from 6 through 50 bars.  Each one is then

 

tested for accuracy of fit to the price data over its own wavelength, instead of the 30 bars of the standard prediction.  Accuracy of fit is determined using standard least-squares correlation, and the best-fitting cycle is displayed and used as the basis for a prediction.

 

The MESA Standard Prediction is based on using the measured dominant cycle, and 30 bars of data are used to measure the Standard Prediction.  By contrast, the MESA Best Fit prediction is useful when a new, shorter cycle is starting to emerge.  Such a shorter cycle can be swamped using data over the longer 30 bar period and therefore does not appear as the dominant cycle.

 

The MESA Best Fit prediction is useful to test the MESA Standard Prediction when the slope of the back-tested Standard prediction does not match the slope of the price action over several bars prior to the cursor prediction.

 

The MESA Best Fit prediction is also carried to the left of the cursor position for one full cycle period of the best-fitting, back-tested cycle, allowing you to compare the cycle prediction to the actual price data.

 

Parameters

The MESA Best Fit study has four adjustable parameters.