Making Numerical Predictions For Time Series Data - Part 1/3

Master Basics To Advance Tools For Predictive Analytics On Time Series Data Using Descriptive Statistics Moving Averages Regressions Machine Learning Neural Networks!

Features Includes:
  • Self-paced with Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App

Course Preview Video

Learn All About Predictive Analytics On Time Series Using Advanced Methods


Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modelling, machine learning, and artificial intelligence to analyse current data to make predictions about future.

One class of Predictive Analytics is to make prediction on Time Series Data. Studying historical data, collected over a period of time, can help in building models using which future can be predicted. For example, from historical data on Temperatures in a City, we can make decent predictions of what the Temperature could be in a future date. Or for that matter, from data collected over a reasonably long period of time regarding various life style aspects of a Diabetic patient, we can predict what should be the volume of Insulin to inject on a given date in future. One example to consider from the Business world could be to predict the Volume of In-Roamers in a Telecom Network in any given period of time in the future from the historical details of In-Roamers in the Network.

The applications are just innumerable as these are applicable in every sphere of business and life.

In this course, we go through various aspects of building Predictive Analytics Models. We start with simple techniques and gradually study very advanced and contemporary techniques. We cover using Descriptive Statistics, Moving Averages, Regressions, Machine Learning and Neural Networks.

This course is a series of 3 parts.

  • In Part 1, we use Excel to make Numerical Predictions from Time Series Data.

We start by using Excel for 2 reasons.

  1. Excel is easy use and thus we can understand complex concepts through exercises that are easy to replicate and thus become easy to understand.
  2. Excel is expected to be available with everyone taking this course.
  • In Part 2, we use R Programming to make Numerical Predictions from Time Series Data.
  • In Part 3, we use Python Programming to make Numerical Predictions from Time Series Data.

The course uses simple data sets to explain the concepts and the theory aspects. As we go through the various techniques, we compare the various techniques. We also understand the circumstances where a particular technique should be applied. We will also use some publicly available data sets to apply the techniques that we will discuss in the course.

From time to time, we will add bonus videos of our real time work on industrial data on which we will apply the Predictive Analytics techniques to create Models for making predictions.

Basic knowledge
  • Basic Knowledge of Statistics
  • Basic Knowledge of Algebra
  • Basic Knowledge of Logarithm
  • Basic Knowledge of Excel

What will you learn
  • Predicting using Descriptive Statistics, Moving Averages, Centred Moving Averages, Weighted Moving Averages
  • Predicting using Linear Regression
  • Predicting using Exponential Regression
  • Predicting using Power Regression
  • Predicting using Logarithmic Regression
  • Predicting using Polynomial Regression
  • Using Excel to make Predictions
  • Using Data Analysis Tool Pak from Excel
  • Using LINEST(), LOGEST(), GROWTH(), TREND() functions in Excel
Course Curriculum
No of Lectures: 35 Total Duration: 05:54:20

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