Calculate the optimal Ad Stock

adstock.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

def broadbent_adstock(grp, r):
    adstock = np.zeros(len(grp))
    adstock[0] = grp[0]
    for i in range(1,len(grp)):
        adstock[i] = grp[i] + r*adstock[i-1]
    return adstock

def optimize_adstock(grp, sales):
    corrs = []
    rates = np.arange(0, 1.01, 0.01)
    rmax = 0
    corrmax = -1

    for r in rates:
            x = broadbent_adstock(grp.copy(), r)
            y = sales
            corr = np.corrcoef(x, y)[0][1]
            corrs.append(corr)
            if corr > corrmax:
                rmax = r
                corrmax = corr
                
    plt.title("Correlation by Decay Rate")
    plt.plot(rates, corrs, label="Correlation")
    plt.xlabel("Decay Rate")
    plt.ylabel("Correlation")
    plt.legend()
    plt.show()
    
    print('忘却率が{0}のとき相関係数は最大値{1}'.format(rmax, corrmax))

Hi Honake, I had been stuck on optimizing the adstock variable for some days. This is brilliant !!

Thank you so much, this is exactly what I was looking for. Great job, thanks for helping out