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Introduction to Time Series Forecasting with Python

Name: Introduction to Time Series Forecasting with Python
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Learn how to frame time series forecasting as a supervised learning problem and apply Python tools like Pandas, Statsmodels, and scikit-learn. This course covers data loading, feature engineering, visualization, resampling, and power transforms to prepare time series data for predictive modeling. By the end, you'll be equipped to build and evaluate forecasting models for real-world applications.

What You'll Learn

Set up a Python environment for time series forecasting with SciPy, Pandas, Statsmodels, and scikit-learn.

Reframe time series data as a supervised learning problem using sliding windows and lag features.

Load, explore, and visualize time series data using Pandas and Matplotlib.

Apply feature engineering techniques such as date-time features, rolling statistics, and expanding windows.

Resample time series data and perform upsampling and downsampling.

Use power transforms like square root and log to stabilize variance and linearize trends.

About this course

Introduction to Time Series Forecasting with Python is a self-paced beginner-level technology online course on GreyAcademy, taught by Anurag Trivedi. It has 8 modules and 39 chapters, and it takes about 10 hours to complete. The course is free to enrol. You get a completion certificate when you finish all the lessons.

Topics covered: Introduction, Copyright, Welcome, Python Environment, What is Time Series Forecasting?, Time Series as Supervised Learning, Load and Explore Time Series Data, Basic Feature Engineering, Data Visualization, Resampling and Interpolation, Power Transforms, Moving Average Smoothing and more.

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