Time Series Forecasting Using Python - eLearning

Time Series Forecasting Using Python - eLearning

Gain a strong foundation in forecasting future trends with the Time Series Forecasting course, designed to help you turn historical data into accurate predictions. This course introduces essential statistical and machine learning techniques used to analyze time-based data and uncover patterns such as trends, seasonality, and cycles.

You’ll explore widely used forecasting models like ARIMA, exponential smoothing, and regression-based approaches, along with modern techniques for improving prediction accuracy. The course also highlights real-world applications across industries such as finance, demand planning, and business analytics.

By the end of the program, you’ll be able to build, evaluate, and apply time series models to solve real forecasting problems and support data-driven decision making.

Key Features

• Course and material in English
• Beginner level
• 5 Hours of On-Demand Videos
• 25 Hands-On Exercises
• 2 Comprehensive Assignments
• 10+ hours recommended study time
• 1 Year access to the learning platform
• Program completion certification included

Learning Outcomes

• Master the core concepts of time series analysis, including its components and stationarity
• Explore multivariate forecasting techniques such as SARIMAX and VAR models
• Use Facebook Prophet for fast and accurate time series forecasting
• Evaluate model performance using key metrics to measure accuracy and reliability
• Analyze real-world time series data using the Yahoo Finance API to extract meaningful financial insights

Target Audience

• Aspiring data scientists and data analysts
• Business analysts working with sales, finance, or operational data
• Software engineers transitioning into data science roles
• Professionals involved in demand planning or forecasting
• Students and graduates exploring analytics or AI careers
• Anyone interested in predictive analytics and time-based data

Prerequisites

• Basic understanding of statistics and probability
• Familiarity with Python or any programming language (preferred but not mandatory)
• Basic knowledge of data handling or Excel
• Analytical and logical thinking skills
• No advanced forecasting experience is required.

Course Content

The Concept of Time Series and Its Components

• The Concept and Necessity of Time Series Analysis
• Granularity, Frequency and Horizon in Time Series Analysis
• Extracting Data Using Yahoo Finance
• Time Series Components: Level, Trend, Seasonality, Cyclicality, And Noise
• Dealing With Missing Value and Outliers in Time Series
• Additive And Multiplicative Decomposition

Dealing with Stationarity

• White Noise
• Random Walk
• The Concept of Stationarity
• Detecting And Handling with Stationarity
• Statistical Test for Detecting Stationarity: KPSS Vs ADF Test
• Granger Causality Test
• Anomaly Detection Using Isolation Forest

Stationarity and Lag Identification

• Autocorrelation and Correlation
• Granger Causality Test
• Autocorrelation Function (ACF)
• Partial Autocorrelation Function (PACF)
• Identification of Lags Using ACF and PACF

Basic Time Series Models

• Naive Method
• Simple Average Method, Moving Average (MA) Model
• Running Prediction with MA Model
• Autoregressive Model (AR)
• Running Prediction with AR Model
• Holt-winter Exponential Smoothing
• Single Exponential Smoothing
• Double Exponential Smoothing

Performance Measurement

• Performance Metrics for Time Series Analysis
• Detecting Performance of the Models
• Compare The Performance of the Models

Advanced Time Series Models

• Autoregressive Moving Average (ARMA) Model
• Running Prediction with ARMA Model
• Autoregressive Integrated Moving Average (ARIMA) Model
• Running Prediction with ARIMA
• Seasonal Autoregressive Integrated Moving Average (SARIMA) Model
• Running Prediction with SARIMA

Multivariate Time Series Analysis

• The Concept of Endogenous and Exogenous Variables
• Introduction to SARIMAX: A Brief Theoretical Background
• Modeling with SARIMAX
• Running Prediction with SARIMAX
• Introduction to VAR
• Modeling with VAR
• Running Prediction with VAR

Time Series Forecasting with Facebook Prophet

• Emergence of Prophet
• Main Parameters in Prophet
• Modeling with Prophet
• Running Prediction with Prophet

FAQ

Will there be any learning material beyond self-paced videos?

Absolutely! The on-demand learning experience goes beyond videos to provide a fully immersive learning environment, including:

• LEARN: Interactive recall quizzes, and real-world case studies to reinforce concepts
• ASSESS: Diagnostic, module-level, and final assessments to track your progress
• PRACTICE: Hands-on exercises with real-world simulations and Cloud Labs
• GAIN INSIGHTS: Real-time analytics and reports highlighting your learning progress, challenges, and suggested areas to revisit for mastering key skills

Can I pursue this course alongside my full-time job?

Yes! This course is designed for maximum flexibility. Delivered in a self-paced online format, it allows you to learn and upskill at your own convenience, making it easy to balance with your full-time job.

Is this course beginner-friendly?

Yes, it starts with fundamentals before moving into advanced forecasting models.

Do I need programming experience?

Basic programming knowledge (especially Python) is helpful but not required.

What industries use time series forecasting?

It is widely used in finance, retail, supply chain, healthcare, energy, and technology for prediction and planning.

Will I learn real-world applications?

Yes, the course focuses on practical forecasting use cases such as sales, demand, and trend prediction.

What techniques will I learn?

You’ll explore methods like ARIMA, exponential smoothing, and other statistical forecasting approaches.

Is this enough to become a forecasting expert?

It provides a strong foundation. Advanced expertise requires further practice with real datasets and machine learning-based forecasting models.