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Introduction to Nixtla for Demand Forecasting.
Data Science   Latest   Machine Learning

Introduction to Nixtla for Demand Forecasting.

Last Updated on December 21, 2023 by Editorial Team

Author(s): Alexandre Warembourg

Originally published on Towards AI.

Through the M5 Demand Forecasting Competition, learn how to use the Nixtla forecasting package.

Accurate forecasting plays a critical role in making informed decisions and staying ahead of the competition in today’s rapidly changing business landscape. Introducing Nixtla Verse, a platform that democratizes access to state-of-the-art predictive models.

Use this system to unlock the potential of advanced statistical models, deep learning, and machine learning for precise forecasts. Suitable for retail, finance, logistics, and any other business reliant on accurate predictions.

Nixtla’s Forecasting package is great for practitioners who want to speed up model benchmarking and development. If you don’t need a highly tailored algorithm to forecast your data, this package can provide very good results most of the time using a scikit-like interface that makes it easy to use.

The ”M” Competitions are a series of open competitions organized by teams led by forecasting researcher Spyros Makridakis and intended to evaluate and compare the accuracy of different forecasting methods: https://www.kaggle.com/c/m5-forecasting-accuracy

In this competition, we use hierarchical sales data from Walmart to forecast daily sales for the next 28 days. The data, covers stores in three US States (California, Texas, and Wisconsin) and includes item level, department, product categories, and store details. In addition, it has explanatory variables such as price, promotions, day of the week, and special events.

Few… Read the full blog for free on Medium.

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