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Master Time Series Forecasting Using Python

Unlock the power of time series analysis and forecasting in Python, gaining the skills to make informed decisions based on data-driven insights and predictions.

Bestseller 6,017+ Learners

Created By Abdullah Karasan

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  • Expert-Taught Videos

  • Guided Hands-On Exercises

  • Outcome Focus

  • Auto-Graded Assessments

  • Recall Quizzes

  • Real-Time Insights

Starts at

100% Money-Back Guarantee

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This Course includes:

  • 5 Hours of On-Demand Videos   
  • 25 Hands-On Exercises 
  • 2 Comprehensive Assignments

Training for Teams of 5+?

Transform your workforce with access to KnowledgeHut's top-rated courses.

    Starts at

    100% Money-Back Guarantee

    This Course is Not Available in your region

    This Course includes:

    • 5 Hours of On-Demand Videos   
    • 25 Hands-On Exercises 
    • 2 Comprehensive Assignments

    Training for Teams of 5+?

    Transform your workforce with access to KnowledgeHut's top-rated courses.

    What You Will Learn

    • Master the fundamental concepts of time series analysis, including components and stationarity.
    • Explore multivariate time series analysis techniques, such as Sarimax and VAR models.
    • Utilize the power of Facebook Prophet for accurate and efficient time series forecasting.
    • Evaluate model performance using key metrics to assess accuracy and reliability of models.
    • Analyze time series data by leveraging the Yahoo Finance API, extracting valuable insights from financial data
    Show More

    The KnowledgeHut Edge

    Superior Outcomes

    Focus on skilled-based outcomes with advanced insights from our state-of-the art learning platform.  

    Immersive Learning

    Go beyond just videos and learn hands-on with guided exercises, assignments and more.  

    World-Class Instructors

    Course instructors and designers from top businesses including Google, Amazon, Twitter and IBM.  

    Real-World Learning

    Get an intimate, insider look at leading companies in the field through real-world case studies.  

    Industry-Vetted Curriculum

     Curriculum primed for industry relevance and developed with guidance from industry advisory boards. 

    Curriculum

    1.

    Learning Objectives:  

    Understand the main concepts of time series analysis, including granularity, frequency, and the components of time series.

    Topics
    • 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 
    2.

    Learning Objectives:  

    Comprehend stationarity concepts, detect stationarity using statistical tests, and apply anomaly detection techniques in time series.

    Topics
    • 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 
    3.

    Learning Objectives:  

    Analyze time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) to identify lags and causality.

    Topics
    • Autocorrelation and Correlation  
    • Granger Causality Test  
    • Autocorrelation Function (ACF)  
    • Partial Autocorrelation Function (PACF)  
    • Identification of Lags Using ACF and PACF 
    4.

    Learning Objectives:  

    Apply basic time series models, such as Naive Method, Moving Average (MA), and Autoregressive Model (AR), to make predictions and evaluate their performance. 

    • 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 
    5.

    Learning Objectives:  

    Use Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) as performance metrics to evaluate the predictive performance of time series models. 

    Topics
    • Performance Metrics for Time Series Analysis  
    • Detecting Performance of the Models 
    • Compare The Performance of the Models  
    6.

    Learning Objectives:  

    Apply advanced time series models, including Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA) to model different types of time series data. 

    Topics
    • 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 
    7.

    Learning Objectives:  

    Master multivariate time series analysis using SARIMAX and Vector AutoRegressive (VAR) Model to forecast and model time series data with multiple explanatory variables.

    Topics
    • 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 
    8.

    Learning Objectives:  

    Utilize the Facebook Prophet library to perform time series forecasting, leveraging the gained knowledge of time series analysis and selecting relevant parameters for the Prophet model.

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

    Prerequisites

    Learners must have basic Python Skills 

    What Learners Are Saying

    The Time Series Forecasting course from KnowledgeHut is highly informative and practical based.

    H
    Huang Mei

    Data Analyst

    The Time Series Forecasting course comprised of comprehensive content and expert instruction.

    I
    Isabella James

    Data Scientist

    Learned practical skills and accurate predictions with KnowledgeHut's Time Series Forecasting course.

    N
    Natalia Romanova

    Operations Research Analyst

    Highly recommended! Gain in-depth knowledge and effective forecasting techniques with KnowledgeHut.

    O
    Olivia Mathews

    Financial Analyst

    Gained valuable insights and practical techniques in KnowledgeHut's Time Series Forecasting course.

    A
    Aiden Thomas

    Market Research Analyst

    How Our Course Compares

    YouTube Videos Online Courses Knowledgehut

    On-Demand Videos

    Immersive Learning Experience

    Structured Curriculum

    Course Curated by Industry Experts

    Auto-Graded Assessments

    Lifetime Access to Courseware

    Course Advisor

    Abdullah Karasan
    Abdullah Karasan

    Senior Data Science Consultant

    Abdullah Karasan is a Data Science Consultant who has worked on prestigious and complex projects including forecasting data congestion, and creating machine learning-based financial risk management tools. 

    Course Advisor

    Abdullah Karasan is a Data Science Consultant who has worked on prestigious and complex projects including forecasting data congestion, and creating machine learning-based financial risk management tools. 

    Abdullah Karasan
    Abdullah Karasan

    Senior Data Science Consultant

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    Frequently Asked Questions

    Yes, you will experience KnowledgeHut's immersive learning in an on-demand format. This will include e-learning material to help you:

      • LEARN with recall quizzes, interactive ebooks, and case studies
      • ASSESS your skills progression with diagnostic, module-level, and final assessments
      • PRACTICE with real-world simulations and Cloud Labs
      • GAIN INSIGHTS with real-time reports and analytics on how you're progressing, your learning challenges, and suggestions of sections to revisit to build competency in the required areas.

      Yes, our online course is designed to give you flexibility to skill up as per your convenience. The course is delivered in a Self-Paced mode so that you can balance your work and learning as per your schedule.

      Yes! Upon passing this online course, you will receive a signed certificate of completion from KnowledgeHut. Thousands of KnowledgeHut alumni use their course certificate to demonstrate skills to employers and their networks.

      KnowledgeHut’s online courses is well-regarded by industry experts, who contribute to our curriculum and use our tech programs to train their own teams.

      You can cancel your enrolment and receive refunds in line with our Cancellations and Refunds policy found at https://www.knowledgehut.com/refund-policy. 

      Yes! Upon passing this course, you will receive a signed certificate of completion from KnowledgeHut. Thousands of KnowledgeHut alumni use their course certificate to demonstrate skills to employers and their networks.

      KnowledgeHut’s courses are well-regarded by industry experts, who contribute to our curriculum and use our tech programs to train their own teams.

      Yes, it does! In the unlikely event that you are not satisfied with the course, and you wish to withdraw within the first seven days, we’ll issue a 100% refund. Refer to our Online Self-Paced Courses Refund Policy for more details.

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