Time Series Analysis & Forecasting

Time Series Analysis & Forecasting – taught by Muhammad Shafiq – is a hands‑on Udemy course that lets you master predictive modeling for temporal data. Updated July 2026, the program covers everything from basic trend detection to advanced Prophet forecasting, giving you practical skills that translate directly into business value. Whether you search for a free time series course, a time series Udemy course, or want to learn forecasting online, this training delivers a clear path to certification and real‑world projects.

What You'll Learn

• Understand core time‑series components such as trend, seasonality, and cycles using Python.
• Analyze time‑series data with exploratory data analysis (EDA) to spot patterns and anomalies.
• Master stationarity testing, autocorrelation, and partial autocorrelation for robust modeling.
• Build AR, MA, and ARIMA models from scratch and interpret their parameters.
• Create SARIMA and Facebook Prophet forecasts for complex seasonal datasets.
• Implement model evaluation techniques, including residual analysis and forecast accuracy metrics.
• Apply forecasting results to business scenarios like sales planning, inventory control, and financial risk.
• Prepare a portfolio‑ready project that demonstrates end‑to‑end time‑series analysis.

Course Details

• Instructor: Muhammad Shafiq
• Rating: 3.5 stars
• Language: English (en‑US)
• Enrolled students: 21,996
• Certificate: Yes, upon completion

What This Course Covers

Introduction to Time Series Concepts

• Definition of time‑series data and its real‑world relevance.
• Identification of trend, seasonality, and cyclic patterns.
• Distinguishing between deterministic and stochastic components.
• Practical examples from finance, retail, and operations.

Exploratory Data Analysis for Temporal Data

• Loading and preprocessing time‑series with Pandas.
• Visualizing data using line plots, heatmaps, and decomposition charts.
• Detecting outliers and missing values specific to chronological datasets.
• Using rolling statistics to assess stability over time.

Classical Statistical Forecasting Models

• Building Autoregressive (AR) and Moving Average (MA) models.
• Combining AR and MA into ARIMA and interpreting order parameters.
• Conducting Dickey‑Fuller tests to verify stationarity.
• Forecasting with confidence intervals and back‑testing results.

Advanced Forecasting with Prophet and SARIMA

• Installing and configuring Facebook Prophet for rapid prototyping.
• Handling holidays, custom seasonality, and changepoints in Prophet.
• Extending ARIMA to Seasonal ARIMA (SARIMA) for multi‑seasonal data.
• Comparing model performance across classical and modern approaches.

Model Evaluation, Selection, and Deployment

• Calculating MAE, RMSE, MAPE, and Theil’s U‑statistic for accuracy.
• Performing cross‑validation on time‑series splits.
• Selecting the best model based on statistical and business criteria.
• Exporting models as Pickle files and creating simple Flask APIs for deployment.

Who Should Take This Course

• Data analysts seeking to add predictive analytics to their toolkit.
• Junior data scientists who need solid time‑series modeling foundations.
• Business intelligence professionals aiming to improve demand forecasting.
• Finance or operations specialists interested in quantitative trend analysis.
• Students preparing for certification exams that include time‑series sections.

Prerequisites

• Basic familiarity with Python programming and libraries such as Pandas and NumPy.
• Understanding of fundamental statistics (mean, variance, correlation).
• No prior experience with time‑series models is required – the course is beginner‑friendly.
• Recommended: prior exposure to data visualization tools (Matplotlib or Seaborn).

Why Enroll in This Course

This training blends theory with extensive hands‑on labs, ensuring you can both build models and explain their decisions. A free coupon provides 100 % off for a limited time, making the Udemy course effectively free until the offer expires. The curriculum stays current with 2026 best practices, giving you an edge over older tutorials and generic online guides.

Course Highlights

• Lifetime access to all video lectures, code notebooks, and practice datasets.
• Self‑paced learning format lets you study whenever you choose.
• Certificate of completion adds credibility to your résumé and LinkedIn profile.
• Real‑world case studies from finance, retail, and supply‑chain domains.
• Mobile‑friendly interface enables learning on phones or tablets.
• 30‑day money‑back guarantee for risk‑free enrollment.

Frequently Asked Questions

Q: Is this course really free?
A: Yes, a free coupon makes the full Udemy course available at 0 USD for a limited period. The discount applies at checkout, so you receive the complete curriculum without paying.

Q: What will I learn in this time series course?
A: You will learn to explore, model, and forecast temporal data using ARIMA, SARIMA, and Prophet, while mastering evaluation metrics and deployment techniques. The skills translate directly to business forecasting, financial analysis, and operations planning.

Q: Do I get a certificate after completing this course?
A: A Udemy‑issued certificate of completion is awarded once you finish all lectures and pass the optional quizzes. This credential can be shared with employers or added to professional profiles.

Q: Is this course suitable for beginners?
A: Absolutely. The instructor starts with fundamental concepts before progressing to advanced models, and no prior time‑series experience is required. Basic Python knowledge is the only prerequisite.

Q: How long do I have to enroll for free?
A: The free coupon is available for a limited time, typically a few weeks, depending on Udemy’s promotion schedule. Enroll before the coupon expires to claim the 100 % discount.