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Sales Forecasting using Time Series Analysis in Python

Learn the nuts and bolts of Sales Forecasting using Time Series Analysis in Python

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What you'll get

  • 2+ Hours
  • 1 Courses
  • Course Completion Certificates
  • One year access
  • Self-paced Courses
  • Technical Support
  • Mobile App Access
  • Case Studies

Synopsis

  • Models/Methods of Forecasting
  • Steps in Forecasting and Problems in Forecasting
  • Time Series Decomposition
  • Simple Forecasting Methods

Content

  • MODULE 1: Essentials Training

    Courses No. of Hours Certificates Details
    Sales Forecasting using Time Series Analysis in Python2h 13m✔

Description

About Sales Forecasting using Time Series Analysis in Python

The Series of data points recorded over a specified period of time is called Time-series data. Time-series analysis is a technique for analyzing time series data and extract meaningful statistical information and characteristics of the data. One of the major objectives of the analysis is to forecast future value. Extrapolation is involved when forecasting with the time series analysis which is extremely complex. But, the forecasted value along with the estimation of uncertainty associated with that can make the result extremely valuable.

Time series analysis is a statistical method to analyse the past data within a given duration of time to forecast the future. It comprises of ordered sequence of data at equally spaced interval. To understand the time series data & the analysis let us consider an example. Consider an example of Airline Passenger data. It has the count of passenger over a period of time.

Importance of Time Series Analysis

Ample of time series data is being generated from a variety of fields. And hence the study time series analysis holds a lot of applications. Let us try to understand the importance of time series analysis in different areas.

  1. Field of Economics: Budget studies, census Analysis, etc.

  2. Field of Finance: Widely used in the field of finance such as to understand the stock market fluctuations, yield management, understand the market volatility, etc.

  3. Social Scientistà: Birth rates or death rates over a period of time and can come with the schemes in their interest.

  4. Healthcare: An epidemiologist might be interested in knowing the number of people infected over the past years. Like in the current situation the researchers might be interested in knowing the people affected by the coronavirus over a period of time. Blood pressure traced over a period of time can be used in evaluating a drug.

  5. Environmental Science: Environmental time series data can help us explain the rise in temperature over the past few years. Plot shows the temperature data over a period of time

Requirements

  • Basic knowledge in statistics, mathematics, programming

Target Audience

  • Anyone interested in learning about sales analytics

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