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Forecasting - Locally Weighted Regression
This is a continuation of the series on forecasting techniques.
Locally weighted linear regression is a non-parametric algorithm, that is, the model does not learn a fixed set of parameters as is done in ordinary linear regression. Rather parameters Θ (theta) are computed individually for each query point x. While computing Θ, a higher “preference” is given to the points in the training set lying in the vicinity of x than the points lying far away from x.
For a detailed discussion see geeksforgeeks.org/ml-locally-weighted-linear-regression/
and for the formula see fawda123.github.io/swmp_workshop_2016/training_modules/module2_wrtds/wrtds.pdf.
Here you can see a shortcut application of this technique to time series with results unexpectedly favorable for price data labelling.
Good at detecting pullbacks. Can be incorporated into a trading system as a signal generator. Alerting is included.
Locally weighted linear regression is a non-parametric algorithm, that is, the model does not learn a fixed set of parameters as is done in ordinary linear regression. Rather parameters Θ (theta) are computed individually for each query point x. While computing Θ, a higher “preference” is given to the points in the training set lying in the vicinity of x than the points lying far away from x.
For a detailed discussion see geeksforgeeks.org/ml-locally-weighted-linear-regression/
and for the formula see fawda123.github.io/swmp_workshop_2016/training_modules/module2_wrtds/wrtds.pdf.
Here you can see a shortcut application of this technique to time series with results unexpectedly favorable for price data labelling.
Good at detecting pullbacks. Can be incorporated into a trading system as a signal generator. Alerting is included.
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