Demand Forecasting Using Time Series
Achieve mastery in time series analysis and ARIMA modeling to effectively predict demand, enhancing decision-making and optimizing resource allocation.
Achieve mastery in time series analysis and ARIMA modeling to effectively predict demand, enhancing decision-making and optimizing resource allocation.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Machine Learning for Supply Chains Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
3.2
(26 ratings)
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What you'll learn
Build ARIMA models for demand prediction
Analyze time series patterns and trends
Implement autocorrelation techniques
Develop regression models for forecasting
Create practical demand prediction systems
Skills you'll gain
This course includes:
1.5 Hours PreRecorded video
5 assignments
Access on Mobile, Desktop, Tablet
FullTime access
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There are 4 modules in this course
This course covers time series analysis and forecasting techniques for supply chain demand prediction. It focuses on ARIMA models and autocorrelation analysis using Python.
Demand Forecasting Using Time Series Module 1
Module 1 · 2 Hours to complete
Demand Forecasting Using Time Series Module 2
Module 2 · 2 Hours to complete
Demand Forecasting Using Time Series Module 3
Module 3 · 2 Hours to complete
Demand Forecasting Using Time Series Module 4
Module 4 · 2 Hours to complete
Fee Structure
Instructors
Head of Data Science, Machine Learning Solutions
Rajvir Dua is the Head of Data Science at LearnQuest, where he combines his expertise in data science with a strong interest in supply chain management. With experience as a teaching and research assistant, he has been actively involved in the data science and consulting startup space. His academic interests are particularly focused on economic problems related to game theory and optimization, which inform his approach to data-driven decision-making in supply chain contexts.On Coursera, Rajvir offers a variety of courses designed to enhance learners' understanding of data science and its applications. His courses include Advanced AI Techniques for the Supply Chain, Fundamentals of Machine Learning for Supply Chain, and Demand Forecasting Using Time Series. These courses aim to equip students with practical skills in machine learning and data analysis, preparing them for challenges in modern supply chain management. Rajvir's commitment to education and his extensive knowledge in data science make him a valuable resource for those looking to advance their careers in this dynamic field.
Co-founder and CEO, Machine Learning Solutions
Neelesh Tiruviluamala is a math professor and the co-founder and CEO of Machine Learning Solutions, with over ten years of consulting experience in the machine learning domain. He has a keen interest in supply chain problems, appreciating the rich data sets that allow for the application of various quantitative tools. His expertise bridges the gap between theoretical mathematics and practical machine learning applications, making him a valuable asset in the field.On Coursera, Neelesh offers a range of courses that focus on advanced machine learning techniques and their applications in supply chain management. His courses include Advanced AI Techniques for the Supply Chain, Demand Forecasting Using Time Series, and Introduction to Data Science and scikit-learn in Python. Through these courses, he aims to equip learners with the skills needed to leverage data science effectively in real-world scenarios, particularly within the context of supply chain optimization. Neelesh's commitment to education and his extensive experience make him an influential figure in the intersection of mathematics and machine learning.
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3.2 course rating
26 ratings
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