I need a Machine Learning / AI (maybe a polynomial regression) formula in PHP to adjust my forecast based on observed data -- 2
Budget: $30 – $250 USD
I need an AI formula to adjust my forecast comparing it to actual observed data.
Please see the attached image that explains it in a visual way.
$X_timeline = [0, 3, 6, 9, 12, 15, ... ,180]; #hours, my forecast has a total of 60 points;
$Y_forecast = [1.7, 1.7, 1.6, 1.6, 1.6, 2.0, 2.3, 1.4, 2.8, 1.3, 1.2, 3.3, 3.2, 3.2, 1.1, 1.1, 1.0, 1.0, ... ,1.1,];
$Y_ai_adjusted_forecast = [ this is the array that I need]; #total of 60 data points to the future;
What I have for the learning model:
$Yp1: forecasted data, model generated in the past;
$Yp2: past actual data, observed
Partial samples:
$Yp1 = [1.7, 1.7, 1.6, 1.6, 1.6, 2.0, 2.3, 1.4, 2.8, 1.3, 1.2, 3.3, 3.2, 3.2, 1.1, 1.1, 1.0, 1.0, ... ,1.1,]; *
$Yp2 = [1.5, 1,6, 1.6, 1.5, 1.4, 1.8, 2.0, 1.6, 2.3, 1.3, 1.1, 2.2, 3.1, 3.0, 1.2, 1.2, 1.1, 1.1, ... , 1.3]; *
* I can go back and generate as many points as needed
If possible, I would like to receive it in PHP format. Any question, please contact me.
Please see the attached image that explains it in a visual way.
$X_timeline = [0, 3, 6, 9, 12, 15, ... ,180]; #hours, my forecast has a total of 60 points;
$Y_forecast = [1.7, 1.7, 1.6, 1.6, 1.6, 2.0, 2.3, 1.4, 2.8, 1.3, 1.2, 3.3, 3.2, 3.2, 1.1, 1.1, 1.0, 1.0, ... ,1.1,];
$Y_ai_adjusted_forecast = [ this is the array that I need]; #total of 60 data points to the future;
What I have for the learning model:
$Yp1: forecasted data, model generated in the past;
$Yp2: past actual data, observed
Partial samples:
$Yp1 = [1.7, 1.7, 1.6, 1.6, 1.6, 2.0, 2.3, 1.4, 2.8, 1.3, 1.2, 3.3, 3.2, 3.2, 1.1, 1.1, 1.0, 1.0, ... ,1.1,]; *
$Yp2 = [1.5, 1,6, 1.6, 1.5, 1.4, 1.8, 2.0, 1.6, 2.3, 1.3, 1.1, 2.2, 3.1, 3.0, 1.2, 1.2, 1.1, 1.1, ... , 1.3]; *
* I can go back and generate as many points as needed
If possible, I would like to receive it in PHP format. Any question, please contact me.