vangja 0.1.2

A time-series forecasting package with an intuitive API capable of modeling short time series with prior knowledge derived from a similar long time series.

vangja

A time-series forecasting package based on Facebook Prophet with an intuitive API capable of modeling short time-series with prior knowledge derived from a similar long time-series.

This package has been inspired by:

• Facebook Prophet
• Facebook Prophet implementation in PyMC3
• TimeSeers
• Modeling short time series with prior knowledge
• Modeling short time series with prior knowledge - PyMC

Installation

You need to create a conda PyMC environment before installing vangja. The recommended way of installing PyMC is by running:

conda create -c conda-forge -n pymc_env python=3.12 "pymc>=5.20.1"

Install vangja with pip:

pip install vangja

Usage

The data used for fitting the models is expected to be in the same format as the data used for fitting the Facebook Prophet model i.e. it should be a pandas dataframe, where the timestamp is stored in column ds and the value is stored in column y.

The API is heavily inspired by TimeSeers. A simple model consisting of a linear trend, a yearly seasonality and a weekly seasonality can be fitted like this:

from vangja import LinearTrend, FourierSeasonality

model = LinearTrend() + FourierSeasonality(365.25, 10) + FourierSeasonality(7, 10)
model.fit(data)
model.predict(365)

Multiplicative compositions

There are two types of multiplicative compositions that vangja supports. The first one supports creating models from components $g(t)$ and $s(t)$ in the form $y(t)=g(t) * (1 + s(t))$. Using vangja, this can be written by using the __pow__ operator:

model = LinearTrend() ** FourierSeasonality(365.25, 10)

The second multiplicative composition supports creating models from components $g(t)$ and $s(t)$ in the form $y(t)=g(t) * s(t)$. Using vangja, this can be written by using the __mul__ operator:

model = LinearTrend() * FourierSeasonality(365.25, 10)

Components

Currently, vangja supports the following components:

• LinearTrend(n_changepoints=25, changepoint_range=0.8, slope_mean=0, slope_sd=5, intercept_mean=0, intercept_sd=5, delta_mean=0, delta_sd=0.05, allow_tune=False)
• FourierSeasonality(period, series_order, beta_mean=0, beta_sd=10, allow_tune=False,tune_method="simple")
• UniformConstant(lower, upper, allow_tune=False)
• BetaConstant(lower, upper, alpha=0.5, beta=0.5, allow_tune=False)
• NormalConstant(mu=0, sd=1, allow_tune=False)

Model tuning

If you are given a long time-series and a "similar" short time-series, you can fit a model on the long time-series and then tune it on the short time-series. This is especially useful if you want to model a long seasonality on the short time-series, but you do not have enough data to do it (e.g. you have 3 months of data and want to model the yearly seasonality). In vangja, this can be written like this:

model = LinearTrend() + FourierSeasonality(365.25, 10, allow_tune=True)
model.fit(long_time_series)
model.tune(short_time_series)
model.predict(365)

Contributing

Pull requests and suggestions are always welcome. Please open an issue on the issue list before submitting in order to avoid doing unnecessary work.