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Introduction to Time Series Forecasting with Python

Name: Introduction to Time Series Forecasting with Python
Price: 499 INR
Availability: InStock

Learn to forecast time series data using Python. This course covers environment setup, data loading and exploration, feature engineering, visualization, resampling, and power transforms. You'll frame time series problems as supervised learning and apply practical techniques to real-world datasets.

What You'll Learn

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

Load, explore, and visualize time series data using Pandas

Transform time series data into supervised learning problems using sliding windows and feature engineering

Apply resampling, interpolation, and power transforms to prepare time series data

Understand the components of time series and common forecasting concerns

Use date/time features, lag features, and rolling statistics to improve forecasts

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 38 chapters, and it takes about 10 hours to complete. Enrol once and learn at your own pace. You get a completion certificate when you finish all the lessons.

Topics covered: Introduction, 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, A Gentle Introduction to White Noise and more.

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