Summary

Here’s a summary of what you have learnt so far in this session:

• First, you began by exploring the different applications of forecasting.
• You then understood that forecasting can be done in two ways:
  • Qualitative forecasting
  • Quantitative forecasting
• After that, you learnt that time series forecasting comes under quantitative forecasting.
• After getting exposed to the Air Passenger traffic problem statement, you learnt the basic steps for forecasting.
  • Define the problem
    • Quantity
    • Granularity
    • Frequency
    • Horizon
  • Collect the data
    • Three important characteristics of time series data:
      • Relevant
      • Accurate
      • Long enough
  • Analyse the data
    • You analysed the Air Passenger traffic dataset and found that the dataset has missing values.
    • Then, you learnt the various methods to handle the missing values and imputed the missing values in the Air Passenger traffic dataset.
    • You also explored some of the methods to detect outliers and understood that Air Passenger traffic dataset does not have any outliers.
    • You also explored the methods to decompose the time series data into its components.
      • Additive Seasonal Decomposition
      • Multiplicative Seasonal Decomposition
  • We will now learn how to build and evaluate the forecast model. (Upcoming Sessions)

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