Master Time Series Forecasting With Python : 2025
Date: 14 February 2026 | Posted by: AD-TEAM
Master Time Series Forecasting With Python : 2025
Published 3/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.81 GB | Duration: 6h 8m
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.
Overview
Section 1: Get Started with Forecasting
Lecture 1 Resources and References
Lecture 2 Environment
Lecture 3 Introduction To Forecasting
Lecture 4 What is Time Series Forecasting
Lecture 5 Create your first time series data Structure in Python
Lecture 6 Assignment solution and Insights :Electricity Consumption Time Series
Lecture 7 Know your Time Series: Components and Decomposition
Lecture 8 Basic Steps in Time Series Forecasting
Lecture 9 Time Series Forecasting Vs Regression Tasks
Section 2: Building Grounds for Time Series Analysis
Lecture 10 Statistical Properties of Time Series - 1
Lecture 11 Autocovariance and Autocorrelation
Lecture 12 White Noise
Lecture 13 ACF Plot and PACF Plot
Lecture 14 Stationarity
Lecture 15 Some simple Time Series Models - MA, AR , RW
Lecture 16 MA Process Explained with Real Life Example
Lecture 17 Stationarity of Moving Average - MA (1)
Lecture 18 AR Process With Example
Lecture 19 AR Process Stationarity AR(1)
Lecture 20 Random Walk , Drift and Properties
Lecture 21 ADF Test for Stationarity - Intution and Interpretation
Lecture 22 Exploring Stationarity and ACF of Simulated Random Walk
Lecture 23 Is Google Stock Price a random walk
Lecture 24 Forecasting A Random Walk
Section 3: Simple Methods of Forecasting
Lecture 25 Hands On Introduction To Baseline Methods
Lecture 26 Average Method - EPS Quarterly Data
Lecture 27 Forecasting Performance Measures
Lecture 28 Consider Trend- Average Recent Data
Lecture 29 Naive Forecast Method - Using Recent Value
Section 4: Hands-On Time Series Forecasting with Statistical Models
Lecture 30 Introduction to ARIMA Models
Lecture 31 Yearly Earthquake - AR(1) modelling
Lecture 32 Model Summary
Lecture 33 Model Diagnostic Plots
Lecture 34 Forecasting Future Data -one step ahead vs muti-step ahead
Lecture 35 Assignment - Model Diagnostic of AR(5) Shampoo Sale Data
Lecture 36 Walk Forward Validation - Introduction - Expanding & Sliding Window
Lecture 37 Walk-Forward Validation with back testing -mutistep forecast
Lecture 38 US Inflation -ARMA Model- Differencing and Inverse Differencing
Lecture 39 Quarterly EPS - JJ: ARIMA Model - Lazy with Differencing
Lecture 40 Apply Box Cox To Reduce Variance - Quarterly EPS ARIMA Model
Lecture 41 Best Selection Order - AIC Criteria
Lecture 42 Seasonal Time Series - Time Series Decomposition
Lecture 43 Taking Seasonality into account- Extending ARIMA To SARIMA
Lecture 44 Let's do together ARIMA Modelling of Tractor Sale Series
Lecture 45 Seasonal ARIMA - SARIMA Modelling with auto arima
Lecture 46 SARIMAX-Forecast GDP with Exogenous Variable - Recursive Forecast
Lecture 47 Assignment - Antidiabetic Drug Prescription Forecast
This course is designed for data enthusiasts, analysts, and professionals who want to master time series forecasting using Python. Whether you're a beginner in time series analysis or an experienced practitioner looking to deepen your understanding of ARIMA, SARIMA, and SARIMAX, this course will equip you with the skills to analyze, model, and forecast real-world data effectively.,Students & Researchers – Gain hands-on experience with real-world datasets and industry applications.,Data Analysts & Scientists – Improve your forecasting skills for business, finance, and operations.,Machine Learning & AI Practitioners – Learn classical time series models before diving into deep learning approaches.,Economists & Financial Analysts – Predict market trends, inflation rates, and sales performance.,Business & Product Managers – Make data-driven decisions with accurate demand forecasting.,If you want to turn raw time series data into powerful business insights, this course is for you!
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