{"id":55251,"date":"2026-04-03T11:50:11","date_gmt":"2026-04-03T11:50:11","guid":{"rendered":"https:\/\/certifeka-edu.com\/programs\/research-for-strategic-development-module-2\/lessons\/lesson-4-quantitative-forecasting-methods-moving-average-forecasting-3-2\/"},"modified":"2026-04-03T11:50:11","modified_gmt":"2026-04-03T11:50:11","slug":"lesson-4-quantitative-forecasting-methods-moving-average-forecasting-3-2","status":"publish","type":"lesson","link":"https:\/\/certifeka-edu.com\/ar\/programs\/research-for-managers-module-ucam-university\/lessons\/lesson-4-quantitative-forecasting-methods-moving-average-forecasting-3-2\/","title":{"rendered":"Lesson 4: Quantitative forecasting methods – Moving average forecasting"},"content":{"rendered":"

\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n

Lesson 4: Forecasting and Predictive Analytics
\n<\/h2>\n

Moving average forecasting<\/h3>\n

Moving average forecasting is a statistical technique used to analyse time series data by calculating the average value of a variable over a specific period of time, with the aim of identifying trends and patterns in the data. In this technique, a moving average (MA) is computed by taking the average of a fixed number of most recent data points in the series.<\/h5>\n
This average is then used to forecast future values of the variable. The length of the period used in the moving average is referred to as the “window size” or “order of the MA”, and can be adjusted to account for different levels of volatility in the data.<\/h5>\n
Moving average forecasting is commonly used in finance, economics, and other fields to make predictions about stock prices, sales figures, and other economic indicators.<\/h5>\n

How to calculate simple moving average:<\/h3>\n
\n\n\t\t\t\t\t 1. Establish the time frame you want to review
\n\t\t\t<\/path><\/svg>
\n\t\t\t<\/path><\/svg>
\n\t\t\t\t\t\t<\/summary>\n
The first step to calculate the simple moving average of a commodity is to consider the length of time in which you want to pull data from. For example, you could determine the desired time frame to be five days, 50 days, 100 days or 200 days.<\/h5>\n<\/details>\n
\n\n\t\t\t\t\t 2. Look at the highest price points for each time interval
\n\t\t\t<\/path><\/svg>
\n\t\t\t<\/path><\/svg>
\n\t\t\t\t\t\t<\/summary>\n
Say you decide to pull price data for seven days.<\/h5>\n
This means that each day, you review stock prices for a commodity and select the highest price point to represent that time interval (one day).
Because you want to determine the simple moving average for seven days, you need to check back on stock prices each day, recording the highest price point for the remaining six days.<\/h5>\n
Here is an example that shows price fluctuations for a coffee brand’s stock:<\/h5>\n
    \n
  • Day one: $8.95<\/li>\n
  • Day two: $8.50<\/li>\n
  • Day three: $8.85<\/li>\n
  • Day four: $9.00<\/li>\n
  • Day five: $8.70<\/li>\n
  • Day six: $8.55<\/li>\n
  • Day seven: $8.65<\/li>\n<\/ul>\n<\/details>\n
    \n\n\t\t\t\t\t 3. Add each price point together
    \n\t\t\t<\/path><\/svg>
    \n\t\t\t<\/path><\/svg>
    \n\t\t\t\t\t\t<\/summary>\n
    Once you record the highest stock prices for a particular commodity over a desired time frame, you need to add each price point to one another.<\/h5>\n
    For example:<\/h5>\n
    $8.95 + $8.50 + $8.85 + $9.00 + $8.70 + $8.55 + $8.65 = $61.20<\/h5>\n

    \u00a0
    \n\t\t\t\t\t<\/details>\n

    \n\n\t\t\t\t\t 4. Divide the total by the number of time intervals established
    \n\t\t\t<\/path><\/svg>
    \n\t\t\t<\/path><\/svg>
    \n\t\t\t\t\t\t<\/summary>\n
    After adding each price point together and getting your answer, you need to divide the total by the number of time intervals you recorded for.<\/h5>\n
    For example:<\/h5>\n
    $61.20\/7 = $8.74<\/h5>\n
    Therefore, $8.74 is the average stock price for a coffee brand over the course of one week.<\/h5>\n<\/details>\n
    The first step to calculate the simple moving average of a commodity is to consider the length of time in which you want to pull data from. For example, you could determine the desired time frame to be five days, 50 days, 100 days or 200 days.<\/h5>\n
    Say you decide to pull price data for seven days.<\/h5>\n
    This means that each day, you review stock prices for a commodity and select the highest price point to represent that time interval (one day).
    Because you want to determine the simple moving average for seven days, you need to check back on stock prices each day, recording the highest price point for the remaining six days.<\/h5>\n
    Here is an example that shows price fluctuations for a coffee brand’s stock:<\/h5>\n
      \n
    • Day one: $8.95<\/li>\n
    • Day two: $8.50<\/li>\n
    • Day three: $8.85<\/li>\n
    • Day four: $9.00<\/li>\n
    • Day five: $8.70<\/li>\n
    • Day six: $8.55<\/li>\n
    • Day seven: $8.65<\/li>\n<\/ul>\n
      Once you record the highest stock prices for a particular commodity over a desired time frame, you need to add each price point to one another.<\/h5>\n
      For example:<\/h5>\n
      $8.95 + $8.50 + $8.85 + $9.00 + $8.70 + $8.55 + $8.65 = $61.20<\/h5>\n

      \u00a0<\/p>\n

      After adding each price point together and getting your answer, you need to divide the total by the number of time intervals you recorded for.<\/h5>\n
      For example:<\/h5>\n
      $61.20\/7 = $8.74<\/h5>\n
      Therefore, $8.74 is the average stock price for a coffee brand over the course of one week.<\/h5>","protected":false},"comment_status":"open","ping_status":"closed","template":"","class_list":["post-55251","lesson","type-lesson","status-publish","hentry"],"yoast_head":"\nLesson 4: Quantitative forecasting methods - Moving average forecasting - Certifeka-edu<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/certifeka-edu.com\/ar\/programs\/research-for-managers-module-ucam-university\/lessons\/lesson-4-quantitative-forecasting-methods-moving-average-forecasting-3-2\/\" \/>\n<meta property=\"og:locale\" content=\"ar_AR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Lesson 4: Quantitative forecasting methods - Moving average forecasting - Certifeka-edu\" \/>\n<meta property=\"og:description\" content=\"Lesson 4: Forecasting and Predictive Analytics Moving average forecasting Moving average forecasting is a statistical technique used to analyse time series data by calculating the average value of a variable over a specific period of time, with the aim of identifying trends and patterns in the data. 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