Demand Forecasting Using Time Series

Go to Course: https://www.coursera.org/learn/demand-prediction-using-time-series

Introduction

### Course Review: Demand Forecasting Using Time Series on Coursera **Course Overview** Demand Forecasting Using Time Series is the second course in the 'Machine Learning for Supply Chain Fundamentals' specialization offered on Coursera. This course is exceptional for learners interested in enhancing their knowledge of time series analysis, particularly for demand prediction, a critical aspect of supply chain management. ### Course Highlights Designed to establish a solid foundation in time series analysis, this course intricately combines theoretical concepts with practical applications. Here’s what you can expect: 1. **Understanding Time Series**: The course begins with an introduction to time series and the various components that define it. This includes essential concepts like stationarity, trend (drift), cyclicality, and seasonality, all of which are vital in understanding the behavior of demand over time. 2. **Correlation and Autocorrelation**: The second module delves into the mathematics of correlation. In time series analysis, the concepts of independence and autocorrelation play a crucial role, as they help in identifying patterns and relationships within the data. 3. **Regression Techniques**: The course builds on foundational concepts of linear regression and extends into time series-specific methods such as lagged regression and ARIMA (AutoRegressive Integrated Moving Average). This module prepares you for employing sophisticated models, including LSTMs (Long Short-Term Memory networks), in advanced analytics. 4. **Practical Applications**: Lastly, the course culminates in a hands-on final project where learners will apply what they've learned to forecast demand using ARIMA models. This practical experience allows students to integrate theory with real-world applications, reinforcing their understanding of demand forecasting. ### Learning Experience The course materials are well-structured and engaging, featuring a blend of video lectures, readings, quizzes, and coding assignments. The use of Python throughout the course is particularly noteworthy, as it equips students with the necessary skills to implement the concepts they learn. Each module progresses logically, ensuring that learners are never overwhelmed and can build their knowledge incrementally. **Instructors**: The instructors are seasoned professionals with expertise in machine learning and supply chain analytics, which instills confidence in the learners regarding the credibility of the content. ### Recommendations Demand Forecasting Using Time Series is highly recommended for: - **Supply Chain Professionals**: If you're working in or aspiring to work in supply chain management, this course equips you with critical skills necessary for effective demand planning and forecasting. - **Data Enthusiasts and Analysts**: This course is ideal if you're looking to deepen your understanding of time series analysis and apply machine learning techniques in data-driven environments. - **Students and Researchers**: Anyone interested in pursuing a career in data science or applied analytics will find the course relevant and enriching. ### Conclusion In conclusion, Demand Forecasting Using Time Series on Coursera is a valuable educational endeavor for anyone aiming to harness the power of time series analysis in their professional toolkit. The clear instruction, practical focus, and rigorous content make it a worthwhile investment for both novice and experienced practitioners in the field. Whether you're enhancing your skills or trying to break into a new domain, this course provides the foundational knowledge and practical experience needed to excel.

Syllabus

A First Glance at Time Series

In this module, we'll get our feet wet with time series in Python. We'll start by getting familiar with where time series fits in to the machine learning landscape. Then, we'll learn about the main types of time series and their distinguishing factors, including period, frequency, and stationarity. After pausing to learn how to plot timeseries in Python, we'll explore the differences between seasonality and cyclicality.

In this module, we'll dive into the ideas behind autocorrelation and independence. We'll start by digging into the math of correlation and how it can be used to characterize the relationship between two variables. Next, we'll define its relationship to independence and explain where these ideas can be used. Finally, we'll combine correlation with time series attributes, such as trend, seasonality, and stationarity to derive autocorrelation. We'll go through both some of the theory behind autocorrelation, and how to code it in Python.

In this module, we'll start by reviewing some of the basic concepts behind linear regression. Then, we'll extend this knowledge to feed into lagged regression, an effective way to use regression techniques on time series. Once we have a solid foothold in basic and lagged regression, we'll explore modern methods such as ARIMA (autoregressive integrated moving average). All of this is building the framework for more advanced machine learning models such as LSTMs (long short-term memory network).

In the final course project, we'll make demand predictions using ARIMA models.