This Python script demonstrates how to perform a multi-step forecast using various linear algorithms for time series data. Here's a breakdown of the main components and functionalities:

Importing Libraries: The script starts by importing necessary libraries including math functions, data manipulation tools (NumPy, Pandas), machine learning models (from Scikit-learn), and visualization tools (Matplotlib).

Data Preparation:

The split_dataset function splits the dataset into training and testing sets, organizing them into weekly windows. The to_series function extracts the total power consumption from each week's data and flattens it into a single series. Model Preparation:

The get_models function defines a dictionary of linear regression models including Linear Regression, Lasso, Ridge, Elastic Net, etc. The make_pipeline function constructs a data preprocessing pipeline including standardization, normalization, and the chosen model. Forecasting:

The forecast function generates a multi-step forecast recursively for each day of the week. The to_supervised function prepares the historical data into input-output pairs suitable for supervised learning. Model Evaluation:

The sklearn_predict function fits a model to historical data and generates forecasts using the pipeline. The evaluate_model function evaluates a model's performance by making forecasts on the test data and calculating RMSE scores. The evaluate_forecasts function calculates RMSE scores for each day and overall. Loading Data and Model Evaluation:

The script loads the dataset, splits it into train and test sets, and defines the number of input days for forecasting (n_input). It then evaluates each defined model using the train-test split and prints out the RMSE scores for each day of the week. Plotting:

Finally, the script plots the RMSE scores for each day of the week for all models using Matplotlib. This script provides a comprehensive framework for comparing the performance of various linear algorithms for multi-step time series forecasting and can be adapted for different datasets and model comparisons.