Comprehensive Guide on Time Series Forecasting with Python

Prefaces:

Table of Content:

• Introduction to Time Series Analysis in Business Intelligence

  • Overview of Time Series and its Applications

  • Common Use Cases: Sales Forecast, Stock Market Trs, etc.

  • Understanding the Dataset: Superstore 2015-2018

    • Data Description Key Insights

Time Series Theory:

Concepts and Basic Principles:

• What is Time Series?

• Importance of Stationarity in Time Series Analysis

  • Stationary vs Non-Stationary Processes

Forecasting Techniques:

• Simple Methods: Moving Average, Exponential Smoothing

• Advanced: ARIMA Auto-Regressive Integrated Moving Averages

  • Seasonal Components and SARIMAX Seasonal Autoregressive Integrated Moving Averages

Time Series Code Implementation:

• Importing Necessary Libraries: Python Ecosystem for Data Analysis Forecasting pandas, numpy, matplotlib, etc.

• Loading Dataset Indexing with Time Series Data

  • Utilizing date as a time index

• Data Processing Techniques: Cleaning, Scaling, Transformation

Visualization and Stationarity Testing:

* Plotting Time Series to Understand Trs

* Statistical Tests for Stationarity `Augmented Dickey-Fuller Test`

* Transformations to Achieve Stationarity Log Transformation, Moving Averages

ARIMA Model Implementation:

• Model Estimation Hyperparameter Selection using auto_arima

  • Tuning Parameters: p, d, q

• Building and Evaluating the ARIMA Model

  • Cross-Validation Techniques for Splitting Data Trn-Test Split

  • Model Evaluation Metrics MAPE

Seasonality Modeling with SARIMAX:

• Handling Seasonality in Time Series Forecasting

  • Understanding Seasonal Components SARIMA vs. ARIMA

• Forecast Evaluation using the pmdarima library for SARIMAX

:

This comprehensive guide demonstrates a practical workflow on conducting time series analysis and forecasting with Python, utilizing datasets from real-world businesses such as 'Office Supplies'. By employing advanced techniques like ARIMAwith an automated parameter selection process and handling seasonality through Seasonal ARIMA SARIMAX, we achieve robust predictions that can inform strategic business decisions. The integration of visualization tools helps in effectively understanding the underlying patterns, while model evaluation ensures reliability and accuracy of forecasts.

This Notebook is designed to be a hands-on resource for practitioners looking to leverage time series analysis in their work, whether it's sales forecasting, inventory management, or market tr prediction.

License

The content within this Notebook has been released under the Apache 2.0 open source license. Feel free to adapt, distribute, and use as part of your projects or studies.

Continued Exploration

Please feel free to dive deeper into specific sections for more detled insights or adjust the code snippets according to your needs in a real-world application setting.

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