Before we dive into the world of forecasting, let’s first understand the different types of forecasting that are or have been prevalent in the industry. There are broadly two types of forecasting. They are:

- Qualitative forecasting
- Quantitative forecasting

Let’s understand the difference between them from Chiranjoy.

Quantitative Forecasting | Qualitative Forecasting |

Based on the data and any repeating historical patterns in the data. | This procedure is taken when data is not available and historical patterns do not repeat. This is based on expert decision |

Captures complex patterns which human cannot identify. | May not capture complex patterns |

No bias | Bias |

E.g. Time series forecasting | E.g. Delphi method |

Clearly, you are in a data science course, and by now you must have understood that we always try and stick to data-driven decision-making. So in this module, you will learn to forecast the future using one of the** quantitative forecasting methods** that is time series forecasting.

Along with learning the concepts of time series forecasting, we will consider a problem statement and apply our concepts that we learn in this module to solve that problem. Let’s quickly understand the problem statement from Chiranjoy.

Before moving to the problem statement, let’s revisit the fundamental terms related to working with time-series data.

**Time Series Data:**Any data that has a time component involved in it is termed as a time-series data. For example, the number of orders made on a food ordering app per day is an example of time-series data.**Time Series Analysis:**Performing analysis on a time-series data to find useful insights and patterns is termed as time series analysis. Let’s take a food ordering app example again. The app might have the data for every day logged in per hour. They might notice that in this data, the number of orders is significantly higher in, say, the 1-2 PM time slot but is significantly lower in the 3-4 PM time slot. This information might be useful for them as they would then be able to estimate the number of delivery boys required at a particular time of the day. Hence, time series analysis is indispensable while working with any time series data.**Time Series Forecasting:**Time series forecasting is basically looking at the past data to make predictions into the future. Say that the food ordering app wants to predict the number of orders per day for the next month in order to plan the resources better. For this, they will look at tons of past data and use it in order to forecast accurately.

This flow is very similar to what you have been doing so far, right? You take data, you analyse it and find insights, and leveraging those insights and some algorithms, you make some predictions on the target variable. In this module we will do exactly the same thing; the only difference being that the word “data” will now be prefixed with “time-series”.

Let’s now understand the problem statement again.

**Air Passenger Traffic Forecasting Problem**: An airline company has the data on the number of passengers that have travelled with them on a particular route for the past few years. Using this data, they want to see if they can forecast the number of passengers for the next twelve months.

Making this forecast could be quite beneficial to the company as it would help them take some crucial decisions like

- What capacity aircraft should they use?
- When should they fly?
- How many air hostesses and pilots do they need?
- How much food should they stock in their inventory?

In this video, you will learn a few terminologies by understanding one of the usages of forecasting in the air passenger traffic example.

**Goal:**A set of business objectives. For example, maximising revenue, maximising capital, etc.**Plan:**A set of actions that a business takes to achieve the goal. In order to come up with a good plan, they need a forecast.**Forecast:**Is the prediction of the future.

Now based on your initial learnings in this segment, answer the questions below.