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Demand Forecasting Using Time Series
  • Level Professional
  • المدة 9 ساعات hours
  • الطبع بواسطة LearnQuest
  • Offered by

عن

This course is the second in a specialization for Machine Learning for Supply Chain Fundamentals. In this course, we explore all aspects of time series, especially for demand prediction. We'll start by gaining a foothold in the basic concepts surrounding time series, including stationarity, trend (drift), cyclicality, and seasonality. Then, we'll spend some time analyzing correlation methods in relation to time series (autocorrelation). In the 2nd half of the course, we'll focus on methods for demand prediction using time series, such as autoregressive models. Finally, we'll conclude with a project, predicting demand using ARIMA models in Python.

الوحدات

Welcome to the Course!

    • 1 Videos
    • 1 Readings
    • 1 Discussion
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1 Discussions

  • Welcome to the Course

1 Videos

  • Course Introduction

1 Readings

  • Machine Learning in Supply Chain

Introduction to Time Series

    • 3 Videos
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3 Videos

  • Module Introduction
  • Introduction to Time Series
  • Datetime Objects in Python

Plotting Time Series with Pandas

    • 1 Videos
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1 Videos

  • Plotting with Pandas

Types of Time Series

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Types of Time Series

1 Videos

  • Types of Time Series

1 Readings

  • Time Series Patterns

Exploratory Analysis of Time Series

    • 1 Videos
    • 1 Readings
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1 Videos

  • Exploratory Analysis with Time Series

1 Readings

  • Time Series Basics

Quiz: Time Series Basics

    • 1 Assignment
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1 Assignment

  • Time Series Basics

Corrrelation

    • 2 Videos
    • 1 Readings
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2 Videos

  • Module Introduction
  • Correlation

1 Readings

  • Correlation

Shifting Time Series

    • 1 Videos
    • 1 Discussion
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1 Discussions

  • Shifting and Resampling

1 Videos

  • Shifting Time Series

Introduction to Autocorrelation

    • 3 Videos
    • 1 Readings
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3 Videos

  • Introduction to Autocorrelation
  • Partial Autocorrelation Function (PACF)
  • PACF Math

1 Readings

  • Autocorrelation Calculator

Autocorrelation and Stationarity

    • 2 Videos
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Autocorrelation and Stationarity

2 Videos

  • Autocorrelation (I)
  • Autocorrelation (II)

Quiz: Correlation with Time Series

    • 1 Assignment
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1 Assignment

  • Correlation with Time Series

Lagged Regression

    • 2 Videos
    • 1 Readings
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2 Videos

  • Module Introduction
  • Lagged Regression

1 Readings

  • Lagged Regression

Autoregressive Models

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Practice Quiz: ARIMA Models

1 Videos

  • Autoregressive Models

ARIMA Models

    • 1 Videos
    • 1 Discussion
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1 Discussions

  • Autoregressive Models vs. Neural Networks

1 Videos

  • ARIMA Models

Coding ARIMA Models

    • 1 Lab
    • 1 Programming
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1 Programming

  • ARIMA Models

1 Labs

  • Programming Assignment Solutions

Final Course Project

    • 1 Lab
    • 1 Programming
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1 Programming

  • Course Project

1 Labs

  • Programming Assignment Solutions

Auto Summary

Enhance your supply chain skills with Coursera's "Demand Forecasting Using Time Series." Led by expert instructors, this professional-level course delves into time series analysis for accurate demand prediction. Over 540 minutes, explore concepts like stationarity, cyclicality, and autoregressive models, culminating in an ARIMA model project in Python. Perfect for data science enthusiasts, it offers flexible starter subscription options.

Rajvir Dua
Instructors

Rajvir Dua

Neelesh Tiruviluamala
Instructors

Neelesh Tiruviluamala