Grab Deal : Flat 30% off on live classes + 2 free self-paced courses! - SCHEDULE CALL

- Data Science Blogs -

Introduction to Regression Analysis & Its Approaches


Regression analysis is an algorithm of machine learning that is used to measure how closely related independent variables relate to a dependent variable. Regression models are highly valuable because they are one of the most common ways to make inferences and predictions. The aim to study regression analysis depends upon the relationship between two variables named as a dependent variable and an independent variable and these are used to make data-driven decisions. The regression models can be of type linear, non-linear, parametric and non-parametric. 

Regression-based models are widely used for forecasting, estimation and also in the interpolation or extrapolation of data. These types of models hence find a great deal of application in Weather prediction, Stock Market, Business Intelligence, etc. Some examples of the regression models are KNN (k-nearest neighbours), multiple linear regression models, logistic regression models, and conventional non-linear regression models. However, the most common regression model is the linear regression, in which the analysts have to draw the line that fits the criterion for a linear mathematical equation. "The method of the least squares" is known to be the earliest form of regression.  

Data Science Training - Using R and Python

  • Detailed Coverage
  • Best-in-class Content
  • Prepared by Industry leaders
  • Latest Technology Covered

Regression models components:

  • Unknown Parameters (can be represented as Scalar or Vector)
  • Dependent Variables (Scalar representation is used)
  • Independent Variables (Vector representation is used) 
  • Error terms (These are not often observed and can be represented as Scalar Terms)

Significance of moving average models- An easy guide

The regression models in their very basic form are just mathematical models that help to solve complex mathematical problems. Therefore, these models may require some assumptions in achieving the results. These assumptions are small but they may lead to some uncertainty which may also lead to false predictions or forecasts. Some analysts came up with the moving average model and the moving average smoothing to deal with this uncertainty or error. A moving average method is a tool for the "Time Series Analysis".

The history of the moving average method goes as early as the 1901s. The initial name given to the method was the "Instantaneous Averages". However, in 1909 the name was changed to "Moving Averages" by R.H. Hooker. 

In the moving average model past forecast errors are used in regression-like models instead of the past values of the forecast. The moving averages can be categorized into two sub-categories:

Read: A Detailed & Easy Explanation of Smoothing Methods
  • Centred moving average
  • Trailing moving average

The centred moving average models are useful in making the trend more visible whereas the trailing moving average model performs better in the case of forecasting. The two models differ in the placement of the window of the average model. The moving average window with width 'w' means for each set of w consecutive values, the average value is calculated. The only job for the analyst is to determine the value of the 'w'. 

The window size may be specific for each task. For example, if an analyst wants to get the local trends, he would keep the size of the window small while a large window size would be required to get the Global trends. 

The moving average is used to smooth out certain fluctuations in the time-series data and make the cycles more visible. However, while dealing with non timed related data, it just smoothens the data.

Yule, a mathematical researcher explained the implication of the special cases of the moving average method in the difference correlation method. 

Below is a sample of the first 5 rows of the dataset, including the header row describing the number of daily female births in California in 1959.

Figure 1: Female Birth Dataset

Read: Deep Learning Tutorial Guide for Beginners

Code for converting a given “Female Birth Dataset” into moving average:

from pandas import read_csv
from matplotlib import pyplot
set = read_csv('daily-total-female-births.csv', header=0, index_col=0)
alpha = set.alpha(window=4)
alpha_mean = alpha.mean()
# plot original and transformed dataset

Output Moving Average Transform

Figure 2: Moving Average Transform

The raw observations are plotted (blue) with the moving average transform overlaid (red).

Why is differencing crucial for the Time Series Analysis?

Differencing, in simple mathematical terms, is the difference between the two consecutive values. The different method is used to remove a pattern or a trend from the data. In statistical terms, it is a stationary transformation applied to the time-series data. However, sometimes differencing fails to smoothen the data and hence differencing is applied one more time and this is known as Second-order differencing.

It is a pre-processing method used to smoothen or filter the data before forecasting or predicting the data. Trends or patterns are needed to be removed from the non-stationary data to achieve stationary data. This stationary data can be further used to make non-ambiguous predictions. These patterns if not removed may lead to a certain bias which may lead to false or unrelated predictions. Other than trends or patterns, differencing can also be used to remove seasonality which also leads to a certain ambiguity in the results. 

Read: An Easy To Interpret Method For Support Vector Machines

Simple exponential Smoothing - The best approach for time series analysis

A simple exponential smoothing method produces very similar results as forecasting with the moving average method. This method is much more cost-effective, flexible and easy to use without affecting its performance. The only difference is that instead of a simple average, a weighted average is taken for all the past values. This helps in assigning more weights to the most recent data and at the same time, the old data is not completely ignored. The simple exponential smoothing can also be used upon the stationary data, i.e. data that do not showcase any trend or pattern. Differencing can be applied to the data to get the desired type of data, as mentioned above. The simple exponential smoothing method collects information based upon the difference between the forecast and the past values. This information helps in correcting future predictions. The forecast value is adjusted based on the smoothing factor. 

Data Science Training - Using R and Python

  • No cost for a Demo Class
  • Industry Expert as your Trainer
  • Available as per your schedule
  • Customer Support Available

The simple smoothing factor can be calculated with just the smoothing factor, previous forecast values and past errors in predictions. This helps in saving a lot of storage space and the computation power as well. This is the reason that this method finds its application in the real-time analysis for the time series data. The smoothing factor "α" (alpha) is determined by the user. This value determines the learning rate. If the smoothing factor is closer to 1, it indicates fast learning and if its value is closer to 0, it indicates slow learning. The value of alpha is chosen based upon the amount of smoothing required.


In conclusion, it is safe to say that differencing is required on a time series data to get accurate and redundant free results. Moving average method and the simple exponential smoothing method can only be applied to the stationary data. Both of the methods have factors (Window width and the smoothing factor) which are determined by the user. These factors help in prioritizing the most recent data over the past data. Both methods will result in the same forecasting results if the value of w is two divided by α -1. The simple exponential smoothing methods prove to be a better method due to its cheap computation and optimized storage capabilities.

Please leave the query and comments in the comment section.

Read: An Ultimate Guide To Python For Data Science (2024)

fbicons FaceBook twitterTwitter lingedinLinkedIn pinterest Pinterest emailEmail


    JanBask Training

    A dynamic, highly professional, and a global online training course provider committed to propelling the next generation of technology learners with a whole new way of training experience.

  • fb-15
  • twitter-15
  • linkedin-15


Trending Courses

Cyber Security Course

Cyber Security

  • Introduction to cybersecurity
  • Cryptography and Secure Communication 
  • Cloud Computing Architectural Framework
  • Security Architectures and Models
Cyber Security Course

Upcoming Class

6 days 19 Jul 2024

QA Course


  • Introduction and Software Testing
  • Software Test Life Cycle
  • Automation Testing and API Testing
  • Selenium framework development using Testing
QA Course

Upcoming Class

-0 day 13 Jul 2024

Salesforce Course


  • Salesforce Configuration Introduction
  • Security & Automation Process
  • Sales & Service Cloud
  • Apex Programming, SOQL & SOSL
Salesforce Course

Upcoming Class

3 days 16 Jul 2024

Business Analyst Course

Business Analyst

  • BA & Stakeholders Overview
  • BPMN, Requirement Elicitation
  • BA Tools & Design Documents
  • Enterprise Analysis, Agile & Scrum
Business Analyst Course

Upcoming Class

7 days 20 Jul 2024

MS SQL Server Course

MS SQL Server

  • Introduction & Database Query
  • Programming, Indexes & System Functions
  • SSIS Package Development Procedures
  • SSRS Report Design
MS SQL Server Course

Upcoming Class

7 days 20 Jul 2024

Data Science Course

Data Science

  • Data Science Introduction
  • Hadoop and Spark Overview
  • Python & Intro to R Programming
  • Machine Learning
Data Science Course

Upcoming Class

6 days 19 Jul 2024

DevOps Course


  • Intro to DevOps
  • GIT and Maven
  • Jenkins & Ansible
  • Docker and Cloud Computing
DevOps Course

Upcoming Class

10 days 23 Jul 2024

Hadoop Course


  • Architecture, HDFS & MapReduce
  • Unix Shell & Apache Pig Installation
  • HIVE Installation & User-Defined Functions
  • SQOOP & Hbase Installation
Hadoop Course

Upcoming Class

-0 day 13 Jul 2024

Python Course


  • Features of Python
  • Python Editors and IDEs
  • Data types and Variables
  • Python File Operation
Python Course

Upcoming Class

14 days 27 Jul 2024

Artificial Intelligence Course

Artificial Intelligence

  • Components of AI
  • Categories of Machine Learning
  • Recurrent Neural Networks
  • Recurrent Neural Networks
Artificial Intelligence Course

Upcoming Class

6 days 19 Jul 2024

Machine Learning Course

Machine Learning

  • Introduction to Machine Learning & Python
  • Machine Learning: Supervised Learning
  • Machine Learning: Unsupervised Learning
Machine Learning Course

Upcoming Class

41 days 23 Aug 2024

 Tableau Course


  • Introduction to Tableau Desktop
  • Data Transformation Methods
  • Configuring tableau server
  • Integration with R & Hadoop
 Tableau Course

Upcoming Class

-0 day 13 Jul 2024

Search Posts


Receive Latest Materials and Offers on Data Science Course