Introduction to the Module
Welcome to the module on ‘Time Series Forecasting – I.‘
You all might have been aware of sales forecasting, weather forecasting, and demand forecasting. You might have seen anchors and stock market experts accurately predicting how the next day, month, or year would turn out in terms of the weather or the prices like they are Nostradamus. But have you ever thought about how they actually make these predictions? In this module, you yourself will learn ways which enable you to forecast by cruising through the beauty of time series forecasting. Let’s quickly understand some more applications of forecasting and the module overview from Chiranjoy.
In the above video, Chiranjoy mentioned the technique of forecasting and how that is helpful in
- Economic Outlook
- Sales Forecasting
- Inventory Planning
- Workforce Planning
- Weather Forecasting
Forecasting is an important technique used in the industry for the above and various other planning tasks to help businesses take proper decisions.
To elaborate further, let’s look at the 3 aspects below where forecasting can help organisations.
- Customer Satisfaction: Forecasting helps in anticipating product requirements in order to ensure on-time deliveries and customer satisfaction.
- Inventory Planning: Demand forecasting also helps in predicting raw material demand to ensure there is no delay in production. Also, ordering raw materials in bulk and keeping finished goods in inventory increases storage costs.
- Sales Planning: Demand forecasting helps in determining future demands or future sales opportunities for products and services. It helps in integrating promotions and improving the flow of goods.
A lack of accurate forecasting is likely to lead to mismanagement of all the future plannings, such as inventory and sales planning and much more.
In this modoie , you will learn the following:
- Defining the time series
- Analysing a time-series dataset to identify patterns
- Build various time series models to forecast
- Evaluate the forecast using various error measures
In this session
In this session, you will start by exploring the different types of forecasting and understand what time series forecasting is. Then you will explore the basic steps for forecasting.
The models that you will learn in Time-Series Forecasting I and II have been performed on an airline passenger traffic dataset. It has the data on the number of passengers that have travelled with the airline 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.
You will be briefed about the dataset and the problem statement again in the subsequent segments. Till then download the dataset for a quick glance below.
Also, please note that the Python notebooks used for all the models in the module of Time Series Forecasting – I have been provided below. But we recommended that you code along with the instructor and come up with your own notebook so that you gain ample practice.
Lecture Notes
For your better understanding of all the concepts taught in the time-series forecasting – I, we have created some notes for you that will help you aid during your study in this module. You can download them and study them from the file attached below.
Guidelines for in-module questions
The in-video and in-content questions for this module are not graded. Note that graded questions are given in a separate session labelled ‘Graded Questions’ at the end of this module. These questions will adhere to the following guidelines:
| First Attempt Marks | Second Attempt Marks | |
| Questions with 2 Attempts | 10 | 5 |
| Questions with 1 Attempt | 10 | 0 |