divina

Date: Mar 07, 2023

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• quickstart
• orchestration
• scale

divina is an open source, BSD3-licensed library providing scalable and automatable multi-horizon forecasting capabilities written in Python.

Ensemble Architecture

divina is essentially a convenience wrapper that facilitates training, prediction, validation and deployment of an ensemble consisting of a machine learning model that is boosted by an endogenous time-series model, allowing for combinations of both types of models to be brought to bear on forecasting problems. divina is also fully integrated with both Dask and Prefect meaning that distributed compute and pipeline orchestration can be enabled with the flip of a switch. For more information of divina’s features, check out the quickstart page.

Installation

divina is available via pypi and can be installed using the python package manager pip as shown below.

pip install divina

Getting Started

To train and predict using a divina pipeline, we first create a pandas dataframe full of dummy data, convert that to a dask dataframe, and call the fit() method of our pipeline. Once the pipeline is fit, it can be used to predict on out-of-sample feature sets.

import dask.dataframe as dd
import pandas as pd

from divina import Divina

example_data = pd.DataFrame(
    data=[
        ["2011-01-01", 3, 6],
        ["2011-01-02", 2, 4],
        ["2011-01-03", 8, 6],
        ["2011-01-04", 1, 1],
        ["2011-01-05", 2, 3],
    ],
    columns=["a", "b", "c"],
)

example_data_dask = dd.from_pandas(example_data, npartitions=1)

example_pipeline = Divina(target="c", time_index="a", frequency="D")

y_hat_insample = example_pipeline.fit(example_data_dask)[
    0
].causal_validation.predictions

y_hat_out_of_sample = example_pipeline.predict(
    example_data_dask.drop(columns="c")
).causal_predictions.predictions