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Why Forecasting ? Why Time Series? Why it is so important ?

Hi everyone I hope everyone is fine. Today I am going to discuss the basics and importance of Time Series forecasting and how it is extensively used in many industries. 



Forecasting is carried out by everyone in day to day life. Some practical examples
  •  A person may forecast the expenses based on the plans and bills in a particular month.
  •  A project manager will forecast the time required for the completion of a project based on the complexity involved.
These things are easier to forecast since we have a clear idea of facts with us. In simple terms, the time of the sunrise tomorrow morning can be forecast precisely. On the other hand, tomorrow’s lottery numbers cannot be forecast with any accuracy. The predictability of an event or a quantity depends on several factors including
  • how well we understand the factors that contribute to it
  • how much data is available
  • whether the forecasts can affect the thing we are trying to forecast


Like an experienced manager can always forecast the deadline accuracy based on previous experience handling projects, understanding the complexity of use cases and his forecasting value can effectively help the company in allocating the resources better.

I think you got some basic understanding of forecasting lets dive into a real-world use case.

REAL-WORLD USE CASE:

A large car fleet company wants to help them forecast vehicle resale values. They purchase new vehicles, rent them out for three years, and then sell them. So if we understand what affects resale values may allow rent and sales policies to be developed in order to maximize profits. With enough historical data, we can greatly increase the profits of the company.

Time Series:

A time series is a sequence of measurements done over time, usually obtained at equally spaced intervals, be it daily, monthly, quarterly or yearly. Time series forecasting comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Suppose you have annual observations for the last few years.


So let us think we have yearly data as mentioned above and the observation column shows the demand for a particular mobile phone company. We will be creating statistical models on the top of data to identify the patterns ( trend, seasonality ) and build a model for forecasting for the next 5 years ( 2017 - 2022). 

Some of the other industries where time series forecasting is used extensively.
  • Forecasting stock’s performance
  • Forecasting interest rate
  • Forecasting weather
  • Budget Analysis
  • Forecasting demand in Manufacturing Industry
  • Forecasting what products are in demand in Retail Industry
Finally,

Good forecasts capture the genuine patterns and relationships which exist in the historical data but do not replicate past events that will not occur again, Many people wrongly assume that forecasts are not possible in a changing environment. Every environment is changing, and a good forecasting model captures the way in which things are changing.

So as discussed I will be sharing a series of blog posts on Time Series every weekend from beginner level to advanced.I will cover all  the topics such as basic statistics,  ETS decomposition, Moving Averages, Exponential Smoothening, ARIMA, SARIMAX etc  and I will use python stack for dealing with Time Series data. So stay connected for more updates.  

References:



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