How to generate random time series data with noise in python 3?

Question

This python 2 code generates random time series data with a certain noise:

from common import arbitrary_timeseries
from commonrandom import  generate_trendy_price
from matplotlib.pyplot import show

ans=arbitrary_timeseries(generate_trendy_price(Nlength=180, Tlength=30, Xamplitude=10.0, Volscale=0.1))
ans.plot()
show()

Output:

Does someone know how I can generate this data in python 3?

What happened when you tried to use the same code in Python 3? — mkrieger1
– mkrieger1, Commented Jun 14, 2021 at 21:09
Does this answer your question? Generate random timeseries data with dates — crissal
– crissal, Commented Jun 14, 2021 at 21:24
@mkrieger1 That the common and commonrandom modules aren't supported in Python 3. — poop
– poop, Commented Jun 14, 2021 at 21:24
@crissal Not really I can't generate noise using that method. But maybe I can add noise later hmmm. — poop
– poop, Commented Jun 14, 2021 at 21:27

Andy Pavlov · Accepted Answer · 2021-06-14 21:42:49Z

1

You can use simple Markov process like this one:

import random

def random_timeseries(initial_value: float, volatility: float, count: int) -> list:
    time_series = [initial_value, ]
    for _ in range(count):
        time_series.append(time_series[-1] + initial_value * random.gauss(0, 1) * volatility)
    return time_series

ts = random_timeseries(1.2, 0.15, 100)

Now you have list with random values which can be zipped with any timestamps.

answered Jun 14, 2021 at 21:42

Andy Pavlov

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