generate a random walk time series

generateRandomWalk.py

import matplotlib.pyplot as plt
import numpy as np

numberOfDays = 7
minutesPerDay = 24 * 60

def addNoise(array, noise=5):
  # some random noise
  return array + np.random.rand( len(array) ) * noise

steps = np.random.standard_normal(minutesPerDay)

# first step is zero
steps[0]=0
oneDayRandomWalk = np.cumsum(steps)

# generate the complete walk concatenating all days
# each day some white noise is added
completeRandomWalk = np.concatenate( [addNoise(oneDayRandomWalk) for _ in range(numberOfDays)] )

## add an upward trend
completeRandomWalk = completeRandomWalk + np.arange(len(completeRandomWalk)) * 0.0015

# I would like all of them to be positive
completeRandomWalk += min(completeRandomWalk)

plt.plot(completeRandomWalk)
plt.show()

#based on this great article, https://towardsdatascience.com/time-series-analysis-creating-synthetic-datasets-cf008208e014