Topic outline

  • General


  • Topic 1


    • Hi, I'm Lee and I am glad that given the opportunity to teach this course in APU. 

    • L0 - Introduction and Overview File

      Let's go through the contents about Time Series Analysis and Forecasting learning outcomes and assessments in this semester. 

  • Topic 2


    • Let's test your understanding of preliminary time series from the questions here.

    • Characteristics of Time Series (Part I) File

      In this week, we are going to learn about the characteristics of time series. These are the learning outcomes:

      1.have a broad understanding of time series characteristics, correlogram, autocorrelation, and stationary time series.
      2.explain the characteristics of time series.
      3.solve the model using computer software and interpret the results.

  • Topic 3


    • There are some important concepts that need your attention in this topic, we learn about the data patterns to explain the characteristics of time series. 
    • Characteristics of Time Series (Part II) File

      In this week, we continue to learn about the characteristics of time series. These are the learning outcomes:

      1.have a broad understanding of time series characteristics, correlogram, autocorrelation, and stationary time series.
      2.explain the characteristics of time series.
      3.solve the model using computer software and interpret the results.

  • Topic 4


    • Starting from this week, we are going to learn some forecasting techniques with calculations and computations for time series data. First, let's try to answer some simple prediction rules from the given questions.

    • Smoothing Techniques (Part I) File

      In this week, we are going to learn about the forecasting techniques of time series. These are the learning outcomes:
      1.have a broad understanding of forecasting techniques, what the most commonly used methods are and how to integrate them into decision making process.
      2.select an appropriate model for time-related data; learn what the methods can and can’t do, what their strengths and weaknesses are; analyse the data, with or without software and interpret the result.
      3.solve the model using computer software and interpret the results.

  • Topic 5


    • In this week, we are continuing to learn some forecasting techniques with calculations and computations for time series data. 

    • Smoothing Techniques (Part II) File

      In this week, we are going to learn about the forecasting techniques of time series. These are the learning outcomes:
      1.have a broad understanding of forecasting techniques, what the most commonly used methods are and how to integrate them into decision making process.
      2.select an appropriate model for time-related data; learn what the methods can and can’t do, what their strengths and weaknesses are; analyse the data, with or without software and interpret the result.
      3.solve the model using computer software and interpret the results.

    • Review Questions Quiz

      Summarise in a short paragraph, all the forecasting techniques learned in this topic. 

  • Topic 6


    • In this week, we proceed to evaluate the performance of forecasting techniques in the lessons earlier. 

    • Performance Evaluation File

      In this week, we look into the performance evaluation of forecasting techniques in the previous lessons.
      1.make data partitioning.
      2.understand the importance of the measurement of errors associated with a forecasting system and how they are used to monitor the forecasting system.
      3.use computer software to solve the problems and interpret the results.

  • Topic 7


    • From this week, we learn for a famous forecasting technique always find it from time series analysis. 

    • Box Jenkins Methodology (Part I) File

      In this week, we are going to learn about the Box Jenkins methodology and the steps of implementation. These are the learning outcomes:

      1. Use Box Jenkins methodology to produce accurate forecasts based on a description of historical patterns in the data.

      2. Solve the model using computer software and interpret the results.


  • Topic 8


    • After we master the skills of building an ARIMA model, now we are continuing to explore seasonal ARIMA model for this week. 

    • Box Jenkins Methodology (Part II) File

      In this week, we continue to learn about the Box Jenkins methodology and the steps of implementation. These are the learning outcomes:

      1. Use Box Jenkins methodology to produce accurate forecasts based on a description of historical patterns in the data.

      2. Solve the model using computer software and interpret the results.


    • The materials from week 1 to week 7 able to help you to answer the assignment. 

  • Topic 9


    • In the last topic of this module, we discover a forecasting technique which is good to forecast financial dataset. 

    • Volatile Models File

      In this week, we look for another time series model that handle financial time series well. These are the learning outcomes:

      1. understand the ARCH and GARCH model estimation.

      2. solve the model using computer software and interpret the results.


    • About time to prepare your class test by every lesson we did from week 1. We are about to start the class test in this week.