/mockseries

Easy and intuitive generation of synthetic timeseries for Python.

Primary LanguageJupyter NotebookMIT LicenseMIT

mockseries

mockseries is and easy to use and intuitive Python package that helps generate synthetic (mock) timeseries.

-> Documentation website.

Installation

#python >=3.6.6 
pip install mockseries

Contributing

Contributions are welcome!
Standards, objectives and process not defined yet.

Quick Run

Define a timeseries

from datetime import timedelta
from mockseries.trend import LinearTrend
from mockseries.seasonality import SinusoidalSeasonality
from mockseries.noise import RedNoise

trend = LinearTrend(coefficient=2, time_unit=timedelta(days=4), flat_base=100)
seasonality = SinusoidalSeasonality(amplitude=20, period=timedelta(days=7)) \
              + SinusoidalSeasonality(amplitude=4, period=timedelta(days=1))
noise = RedNoise(mean=0, std=3, correlation=0.5)

timeseries = trend + seasonality + noise

Generate values

from datetime import datetime
from mockseries.utils import datetime_range

ts_index = datetime_range(
    granularity=timedelta(hours=1),
    start_time=datetime(2021, 5, 31),
    end_time=datetime(2021, 8, 30),
)
ts_values = timeseries.generate(ts_index)

Plot or write to csv

from mockseries.utils import plot_timeseries, write_csv

print(ts_index, ts_values)
plot_timeseries(ts_index, ts_values, save_path="hello_mockseries.png")
write_csv(ts_index, ts_values, "hello_mockseries.csv")

References