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Time Series Analysis And Forecasting Using Python

person icon Abhishek And Pukhraj

4.1

Time Series Analysis And Forecasting Using Python

Master time series analysis & forecasting models in Python along with Time Data Visualization, AR, MA, ARIMA, Regression, and ANN

updated on icon Updated on Jun, 2024

language icon Language - English

person icon Abhishek And Pukhraj

English [CC]

category icon Development,Python

Lectures -95

Resources -2

Duration -13 hours

4.1

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Course Description

Time Series Analysis And Forecasting Using Python is a comprehensive Time Series Forecasting course that helps you make decisions on how to manage your inventory, plan your workforce, and many other business-related tasks.

The course offers training on time series analysis and forecasting needed to know about various forecasting models and how to use them in Python.

Time Series Analysis And Forecasting Using Python Overview

This online course will teach you how to implement time series forecasting models such as AutoRegression, Moving Average, ARIMA, SARIMA, etc. You will also learn how to implement multivariate forecasting models based on Linear regression and Neural Networks. By the end of the course, you will be able to confidently practice, discuss, and understand different Forecasting models used by organizations

How this course will help you?

All participants in this course receive a Verifiable Certificate of Completion.

This course will provide you with a solid foundation by instructing you on the most popular forecasting models. It teaches how to use them if you're a business manager, executive, or student who wants to study and apply forecasting models in real-world business situations.

Why should you choose this course?

The course walks you through the curriculum by teaching with examples. The main goal of each Part is to teach you the ideas through practical examples. Each section has the following components:

  • Use cases and theoretical concepts of various forecasting models

  • Detailed instructions for putting forecasting models into practice in Python

  • Downloadable code files with data and solutions from each lecture

  • Lecture notes and assignments to review and apply the principles

This course stands out from other online courses due to the practical classes where we develop the models for each of these tactics.

Who this course is for:

  • Individuals who want to work in data science

  • Professionals in the workforce just starting their machine learning career

  • Statisticians should gain more relevant experience.

  • Anyone who desires to quickly learn Time Series Analysis using Python

Goals

  • Get a firm grasp of time series analysis and forecasting.

  • Recognize the business contexts in which time series analysis can be used.

  • Learning about Auto Regression and Moving Average Models by Creating 5 Different Time Series Forecasting Models in Python

  • Learn about the forecasting models SARIMA and ARIMA.

  • To manage Time Series data and perform statistical calculations, use Pandas DataFrames.

Prerequisites

  • Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same.

Time Series Analysis And Forecasting Using Python

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction
1 Lectures
  • play icon Introduction 02:10 02:10
Time Series - Basics
5 Lectures
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Setting up Python and Python Crash Course
9 Lectures
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Time Series - Data Loading
1 Lectures
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Time Series - Visualization
2 Lectures
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Time Series - Feature Engineering
2 Lectures
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Time Series - Resampling
2 Lectures
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Time Series - Transformation
3 Lectures
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Time Series - Important Concepts
5 Lectures
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Time Series - Test Train Split
1 Lectures
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Time Series - Naive (Persistence) model
1 Lectures
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Time Series - Auto Regression Model
3 Lectures
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Time Series - Moving Average model
2 Lectures
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Time Series - ARIMA model
4 Lectures
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Time Series - SARIMA model
2 Lectures
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Stationary time Series
1 Lectures
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Linear Regression - Data Preprocessing
20 Lectures
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Linear Regression - Model Creation
12 Lectures
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Introduction to ANN
1 Lectures
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Single Cells - Perceptron and Sigmoid Neuron
3 Lectures
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Neural Networks - Stacking cells to create network
3 Lectures
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Important concepts: Common Interview questions
1 Lectures
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Standard Model Parameters
1 Lectures
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Tensorflow and Keras
2 Lectures
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Python - Dataset for classification problem
2 Lectures
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Python - Building and training the Model
4 Lectures
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Python - Solving a Regression problem using ANN
1 Lectures
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Instructor Details

Abhishek and Pukhraj

Abhishek and Pukhraj


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Quazi Faruq

It is a good course with easy-to-understand step-by-step instructions.

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