Forecasting App using univariate TSA models

Sandbox project to dynamically test custom time series forecasting models in a small business dataset using statsmodels, sklearn and streamlit.

Table of Contents
Introduction
Features
Getting Started
- Prerequisites
- Installation
Deployment

Introduction

Project is divided in streamlit components in st_components folder and estimators components in tsa_estimators folder.

File structure

project/
│
├── st_components/
│   ├── charts.py
│   ├── selectbox.py
│
├── tsa_estimators/
│   ├── _build_model.py_
│   ├── custom_estimators.py_
│   └── base_estimator.py
│
├── _streamlit_app.py
├── utils.py
├── requirements.txt
└── README.md

Design for dynamical models

tsa_estimators folder defines the main classes for this project. Since the purpose was early on established as evaluating different models using the same metrics and functions, an abstract class was created as template and each estimator class was then derived from this class.
This provides a base line to dynamically add new models and to compare such models using
The build_model function is used to concat the results from every model and return a list of dataframes which provides a very good interface for streamlit.

  +------+        +------+          +------+
|BaseModel|     |BaseModel|        |BaseModel|
  +------+        +------+          +------+
       \          /               /
        \        /           /
         +------+       /
       build_models /
         +------+
              \
               \
                \
                +------+
                 Output
                +------+

Scope of the project

Compare the results of different TSA models
Provide a custom sandbox framework
- Create custom TSA models
- Add your own model params and aliases
- Define the metrics you want (full support to sklearn regressor metrics)
- Define stacking regression models using TSA as base models
Residuals plot for normality check
Forecasting and model params

Not in scope

Optimization of model params (such as GridSearchCV)
Normal tests for residuals (such as Shapiro Wilk)
Homoscedasticity tests for residuals
Extensive user interface to add and customize models outside of python script
Standard errors of each model (it would make the UI cluttered) but you can easily implement it via implementation of BaseEstimator.get_stderr method

Features

Custom model constructor

Supporting all statsmodels.tsa models as well as others:

Holt
Holt Winters
SimpleExpSmoothing
ARMA
ARIMA

Stacking model constructor with TSA as base models

Supports any sklearn.linear_model class:

ElasticNet
Ridge
Lasso
LinearRegression
more to confirm (in theory all should be supported)

Getting Started

Passing models constructor object list

Each model should be passed as a list [model_object: statsmodels.tsa, alias]. If no model alias is given, consider an empty string.

# models: list[[model_object: statsmodels.tsa, alias: str]]
__models = [[SimpleExpSmoothing(endog = target, 
                                initialization_method="estimated"),
                                ""],
            [Holt(endog = target,
                    initialization_method = "estimated", 
                    exponential = True),
                    "Holt-model"],
            [ExponentialSmoothing(endog = target, 
                                    seasonal_periods=12, 
                                    trend="add", 
                                    seasonal="add", 
                                    use_boxcox=False, 
                                    initialization_method="estimated"),
                                    "HW-Additive"],
            [ExponentialSmoothing(endog = target, 
                                    seasonal_periods=12, 
                                    trend="add", 
                                    seasonal="mul", 
                                    use_boxcox=False, 
                                    initialization_method="estimated"),
                                    "HW-Multiplicative"],
            [ExponentialSmoothing(endog = target, 
                                    seasonal_periods=12, 
                                    trend="add", 
                                    seasonal="add", 
                                    use_boxcox=False,
                                    damped_trend = True,
                                    initialization_method="estimated"),
                                    "HW-Additive-damped-trend"],
            [ExponentialSmoothing(endog = target, 
                                    seasonal_periods=12, 
                                    trend="add", 
                                    seasonal="mul", 
                                    use_boxcox=False,
                                    damped_trend = True,
                                    initialization_method="estimated"),
                                    "HW-Multiplicative-damped-trend"]]

Using stacked models

Stacked models can't be dynamically implemented, but this is how they work.

# stacking estimator
se = StackedEstimator(meta_classifier = LinearRegression(),
                    stackeddataset = _pred,
                    target = target,
                    alias = "Stacking estimator (LR)"
                    )

meta_classifier receives an instance of the meta classifier class ([see supported classifiers](#Passing models constructor object list)), stackeddataset param receives the predictions dataframe from all the base estimators and target references the target variable.

Prerequisites

The project was built on Python 3.10.6.

Installation

# Example installation commands
git clone https://github.com/lmao420blazeit/CPBS-TSA-Forecast.git
cd yourproject
pip install -r requirements.txt
run in console: streamlit run _streamlit_app.py

Deployment

The project was deployed using streamlit native serverless.

miguelsiloli / CPBS-TSA-Forecast