Full Stack Time Series Forecasting Masterclass
Published 7/2026
Created by Dr. Salochina Oad
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 66 Lectures ( 3h 14m ) | Size: 1.4 GB
Learn time series forecasting with hands‑on Python projects
What you'll learn
Identify key components like trend, seasonality, noise, and anomalies in temporal data Build and evaluate models such as ARIMA, SARIMA, Prophet, and deep learning architectures (LSTM, GRU) for real‑world datasets Implement forecasting workflows using Pandas, NumPy, Scikit‑learn, Matplotlib, and optimized data structures for scalable time series processing Apply parametric and non‑parametric approaches to improve model interpretability and performance Visualize predictions, assess accuracy, and present insights for business decision‑makingRequirements
Basic Python knowledgeDescription
Learn how to forecast trends, patterns, and future values using Python in this practical, full‑stack time series forecasting masterclass. Designed for beginners and growing into intermediate and advanced techniques, this course teaches you how to analyze time‑based data, uncover insights, and build accurate forecasting models using real datasets.
You'll start with time series foundations, exploratory data analysis, and diagnostics. Then you'll build classical forecasting models such as ARIMA and SARIMAX, followed by business‑focused forecasting using Prophet and machine learning algorithms. As you progress, you'll explore deep learning approaches including CNNs, LSTMs, and GRUs to model complex sequences and improve prediction accuracy. The course concludes with anomaly detection techniques used in modern forecasting systems.
Every module includes hands‑on Python projects and downloadable notebooks so you can apply what you learn immediately. Whether you're a data analyst, student, or developer, you'll gain practical skills to make confident, data‑driven decisions. You'll also learn how to evaluate model performance, compare forecasting approaches, and choose the right technique for different types of time‑series problems, ensuring stronger results across diverse datasets and scenarios for better forecasting outcomes. By the end, you'll have a complete workflow you can reuse in real‑world forecasting tasks.
Start forecasting smarter today.
Who this course is for
This course is ideal for beginner data analysts, aspiring data scientists, business professionals working with data, students learning forecasting, Python learners wanting real projects, and anyone curious about time series forecasting.Homepage
Code:
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