Predicting The Future in Data Science: Introduction to Forecasting Analysis

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Have you ever thought about how businesses succeed in this competitive world? What do they do to always remain on the top of their niches? And what strategies do they follow to be on that list? Maybe all these look simple to you while you read or go through them, but it involves numerous complicated processes done precisely to get that kind of result. 

But if you are still thinking about predicting the upcoming demand in the market and bringing those products into the market, forecasting or time series analysis plays a crucial role from start to end to taking calculated risks, driving sales and profits to the business. 

In this blog, we will walk through forecasting analysis, a.k.a time series analysis for predicting the future in data science. Let us dive in to gain in-depth knowledge about forecasting. 

What Is Forecasting or Time Series Analysis? 

Forecasting is the process of predicting and estimating the future based on past data. They analyze the data, discover the massive dataset’s hidden patterns, go through the past data, and predict the future. 

Before we dive further, let us understand how forecasting is applicable in various ways. 

Forecasting is a critical factor for any business and organization. Whether to plan for new products or expand your business territory or how much you must have your best-selling products in the inventory, forecasting or time series analysis plays a crucial role in determining success and failure or making critical decisions. 

It can be predicting the investment in your business or sharing people’s hold on your business in the market, how they can be turned over the years, forecasting, and expecting many things. 

Yes, some things are indeed easier to forecast than others. Like time for sunrise or change in weather. But who will win the lottery cannot get predicted, as it depends on the number of events or as many factors involved there? Some of the critical questions to consider are:

  • How well you know different factors involved in the process are well contributing.
  • How much data is available? Whether they are right or wrong to predict for accuracy.
  • Does it affect the forecast if we try to predict the outcomes? 

Suppose you want to forecast the demand for air conditioners in the summer. In that case, you can easily predict based on the locations, the temperature of that place, total expenditure while setting up, and income of the people, and what all features they like to go. Using these data, you can easily predict the demand for air conditioners this summer in India across various locations.  

On the other hand, if you work on stock and investment, there is always a higher risk, huge fluctuations. The number you see might increase, and you see higher chances of going up, but again you see going down in no time. Therefore, analyzing past historical data, keeping them on the watch list, and investing in them would always be an optimal option. 

Four Most Common Types of Forecasting Methods: 

Straight Line Method

This method is one of the to-go and straightforward forecasting methods. This method has the most uses in financial analysis to predict future revenue using historical figures and trends. It focuses on measuring the constant growth rate. 

Moving Average Method

Moving average is a smoothing technique that looks for underlying patterns under the data set to predict the future value. The most common examples are three months, five months moving averages that focus on the repeated forecast.

Simple Linear Regression Method

Simple regression analysis is a widely used tool for analyzing the relationship between the variables for prediction purposes. The prime objective of this method is to compare one independent variable with one dependent variable.  

Multiple Linear Regression Method

Multiple linear regression is a widely used tool for analyzing and forecasting when two or more independent variables with one dependent variable for projection. 

Final Words

Forecasting or time series analysis is a crucial method to analyze data for predicting the future based on the historical data to find the hidden patterns on trending to produce better products based on the demand, drive more sales, and increase the ROI of the business. 

In this blog, you learned about what forecasting is and its role in today’s data for predicting the hidden trends and possibilities to do well in the competitive market. You also learned about a few practical cases, along with four types of forecasting methods to sum up with the blog. 



Palak Airon

Article by Palak

Data Scientist personnel with over 8 years of professional experience in the IT industry. Competent in Data Science and Digital Marketing. Expertise in professionally researched Technical Content Writing. Read More Time Series Analysis

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