fcst 0.1.0

Forecasting utilities, time-series preprocessing, rolling back-test and ensemble forecasting from model selection

fcst

Package repo on PyPI: fcst - PyPI

Installation

uv add fcst

Features

This package provides you with these sub-modules

1. automation This automatically runs back-test, select the best models, and forecast for you. You can customise whether or not to run in parallel, how many top models to select, etc.
2. forecasting This provides you with the basic functionality of fit() and predict(), given that you pass in the model.
3. evaluation This provides you with back-test and model selection functionalities.
4. preprocessing This allows you to prepare your dataframes, preprocess the time-series data, fill in the missing dates automatically.
5. horizon This is an API for dealing with future horizon from sktime. But in some modules, it will also do this automatically.
6. models Gives you the base models for you to work with. Provides you with the basic models, default (fallback) and zero predictor.
7. metrics Our own implementation of forecasting performance metrics.
8. common Other common functionalities, e.g., types.

Usage

Examples

from fcst.automation import run_forecasting_automation
from fcst.preprocessing.dataframe import prepare_forecasting_df
from fcst.models import base_models
import pandas as pd


df_input = pd.read_csv("path-to-your/file.csv")

data_period_date = pd.Period("2025-02", freq="M")

df_forecasting = prepare_forecasting_df(
    df_raw=df_input,
    min_cap=0,  # Cap the value to not go under 0
    freq="M",
)

df_forecasting_results = run_forecasting_automation(
    df_forecasting,
    value_col=net_amount,
    data_period_date=data_period_date,
    models=base_models,
    backtest_periods=5,
    eval_periods=2,
    top_n=2,
    forecasting_periods=3,
    parallel=True,
)

# Do something with the results
def format_and_upload_results(df_results):
    ...