Master Time Series Forecasting with Python : 2025

Published 3/2025
Created by Anuradha Agarwal
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 47 Lectures ( 6h 7m ) | Size: 2.42 GB

Learn ARIMA, SARIMA, and SARIMAX from scratch—master time series forecasting, model diagnostics, real-world application

What you’ll learn
Understand Time Series Fundamentals – Grasp key concepts like trend, seasonality, stationarity, and autocorrelation.
Apply Classical Forecasting Models – Master ARIMA, SARIMA, and SARIMAX for short-term and long-term forecasting.
Preprocess & Transform Data – Handle missing values, apply differencing, Box-Cox transformations, and ensure stationarity.
Evaluate & Optimize Models – Use AIC, BIC, RMSE, and residual diagnostics to fine-tune forecasts for real-world accuracy.

Requirements
Basic Python programming knowledge
Beginner Level Familiarity with data analysis libraries like Pandas & NumPy
No prior time series experience needed—everything is explained from the ground up!

Description
In this engaging and hands-on course, you will master time series forecasting using Python, focusing on real-world applications. You’ll begin by understanding the core concepts of time series data, including trend, seasonality, noise, and stationarity. Learn why stationarity is critical for accurate modeling and how to transform non-stationary data using differencing, log transformations, and seasonal adjustments.The course dives into essential forecasting techniques such as ARIMA, SARIMA, and SARIMAX, along with the mathematical intuition behind these models. You’ll gain a deep understanding of autocorrelation, partial autocorrelation, and how to interpret model parameters to optimize forecasting accuracy and prediction power.Through practical exercises, you’ll learn how to preprocess and visualize time series data, handle missing values, and apply transformations. You will also gain hands-on experience with model selection, diagnostics, and evaluation metrics like MAE, RMSE, and AIC, helping you understand the strengths and limitations of different models.The course covers rolling and recursive forecast approach, preparing you to predict unknown future data effectively. The significance of model evaluation will be highlighted throughout, ensuring your forecasting models are reliable. By the end of this course, you’ll be equipped to tackle real-world forecasting challenges, from sales predictions to financial forecasting. With interactive tutorials, step-by-step projects, and real-world datasets, you’ll confidently build and evaluate forecasting models in Python, gaining a solid foundation in both the theory and practice of time series analysis.