Metadata-Version: 2.4
Name: fcst
Version: 0.6.0
Summary: Forecasting utilities, time-series preprocessing, rolling back-test and ensemble forecasting from model selection
Project-URL: Repository, https://github.com/anuponwa/fcst
Author-email: Anupong Wannakrairot <anuponwa@scg.com>
License: MIT License
        
        Copyright (c) 2024 Anupong Wannakrairot
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in
        all copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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        THE SOFTWARE.
License-File: LICENSE
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.10
Requires-Dist: autots>=0.6.21
Requires-Dist: pandas>=2.2.3
Requires-Dist: prophet>=1.1.6
Requires-Dist: sktime>=0.36.0
Requires-Dist: statsforecast>=2.0.1
Requires-Dist: statsmodels>=0.14.4
Description-Content-Type: text/markdown

# fcst
![Publish Tag to PyPI](https://github.com/anuponwa/fcst/actions/workflows/publish-tag-to-pypi.yml/badge.svg)

Package repo on PyPI: [fcst - PyPI](https://pypi.org/project/fcst/)

## Installation
```bash
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**

In case you want to automate the whole process...

```python
from fcst.automation import run_forecasting_automation
import pandas as pd


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

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

results = run_forecasting_automation(
    df_raw=df_input,
    date_col="date",
    value_col="net_amount",
    data_period_date=data_period_date,
    backtest_periods=3,
    eval_periods=2,
    top_n = 2,
    forecasting_periods=2,
    id_cols=["customer_code", "product_code"],  # In case the unit of analysis is: customer, product
    min_cap=0,  # clean the value for some rows that have < 0
    freq="M",  # Monthly forecast
    parallel=True,
)

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

```python
from fcst.automation import run_forecasting_automation
import pandas as pd


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

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

results = run_forecasting_automation(
    df_raw=df_input,
    date_col="date",
    value_col="net_amount",
    data_period_date=data_period_date,
    backtest_periods=3,
    eval_periods=2,
    top_n = 2,
    forecasting_periods=2,
    id_cols=None,  # In case you want to predict the whold dataframe
    min_cap=0,  # clean the value for some rows that have < 0
    freq="M",  # Monthly forecast
    parallel=True,
)

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

Other utilities for time-series

```python
from fcst.preprocessing import prepare_timeseries


# Group time-series based on customer-product, then yields a generater of (id_, pd.Series)
timeseries = prepare_timeseries(
    df_raw=df_input,
    date_col="date",
    value_col="net_amount",
    data_period_date=data_period_date,
    id_cols=["customer_code", "product_code"],
)


# Returns the whole DataFrame as a time-series
timeseries = prepare_timeseries(
    df_raw=df_input,
    date_col="date",
    value_col="net_amount",
    data_period_date=data_period_date,
    id_cols=None,
)
```

There are also these two functions from sub-modules, if you want to explore:
* `from fcst.preprocessing.dataframe import prepare_forecasting_df`
* `from fcst.preprocessing.timeseries import extract_timeseries`


## More detailed usage

### Automation

This `automation` sub-module runs cleaning, fill-in the missing dates, evaluation, model selection, forecasting and ensemble, everything automatically.


### Preprocessing

This provides time-series preparation functions. The most complete function and does most of the heavy lifting is `prepare_timeseries()` function.
You can import it from `from fcst.preprocessing import prepare_timeseries`.
You can specify ID columns, date column, value column to forecast, the frequency, and much more.
This function returns either a single time-series or a generator, depending on the `id_cols` parameter.

If you want to go into details or want full control in each step, you can use these two functions:
* `from fcst.preprocessing.dataframe import prepare_forecasting_df`
* `from fcst.preprocessing.timeseries import extract_timeseries`


### Models

By default the `run_forecasting_automation()` uses `base_models` from `models`.
But you can define your own model(s) with `fit()` and `predict()` methods.
You can get the `base_model` and put your own model(s) to the dictionary.


### Metrics

By default, we use `mape()` for measuring accuracy (error) of forecasting models.
We define our own as it handles when the forecast or actual values are 0.
Our `mape()` function also has `symmetric` flag to calculate the sMAPE.


### Horizon

If you want to utilise the forecast horizons or get some future dates, this sub-module provides the basic functionalities.
And this is the base of `forecasting` and `evaluation` functions.


### Forecasting

In `forecasting` sub-module, you can use `forecast` or `ensemble` to forecast using one or more models.


### Evaluation

The `evaluation` sub-module provides the back-testing and model selection functions.
You can pass in a model dictionary to evaluate which models are suitable for each time-series.

