Demand Forecasting Using Time Series

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Demand Forecasting Using Time Series

Coursera · Beginner ·📐 ML Fundamentals ·3mo ago

Key Takeaways

Explains how to analyze time series data for demand forecasting, including stationarity, trend, and seasonality

Original Description

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.
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