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

# Single season multiplier factors - for seasonality effect
seasonal_multipliers = [1.1, 1.3, 1.2, 1.5, 1.9, 2.3, 2.1, 2.8, 2.0, 1.7, 1.5, 1.2]
# Immitate 10 years of data
xs = np.arange(1, 121)

time_series = []
# Split to 10 chunks - 1 year each
for chunk in np.split(xs, 10):
    for i, val in enumerate(chunk):
        # Multiply value with seasonal scalar
        time_series.append(float(val * seasonal_multipliers[i]))

x = pd.date_range(start="2015-01-01", freq="MS", periods=120)
y = time_series      

print(x[-10:])
print(y[-10:])