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Published inNov 2022
PublisherPackt
ISBN-139781803246802
Edition1st Edition
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Manu Joseph
Manu Joseph
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Manu Joseph

Manu Joseph is a self-made data scientist with more than a decade of experience working with many Fortune 500 companies enabling digital and AI transformations, specifically in machine learning-based demand forecasting. He is considered an expert, thought leader, and strong voice in the world of time series forecasting. Currently, Manu leads applied research at Thoucentric, where he advances research by bringing cutting-edge AI technologies to the industry. He is also an active open-source contributor and developed an open-source library—PyTorch Tabular—which makes deep learning for tabular data easy and accessible. Originally from Thiruvananthapuram, India, Manu currently resides in Bengaluru, India, with his wife and son
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Mapping additional information

From the data model that we prepared earlier, we know that there are three key files that we have to map: Household Information, Weather, and Bank Holidays.

The informations_households.csv file contains metadata about the household. There are static features that are not dependent on time. For this, we just need to left merge informations_households.csv to the compact form based on LCLid, which is the time series identifier.

Best practice

While doing a pandas merge, one of the most common and unexpected outcomes is that the number of rows before and after the operation is not the same (even if you are doing a left merge). This typically happens because there are duplicates in the keys on which you are merging. As a best practice, you can use the validate parameter in the pandas merge, which takes in inputs such as one_to_one and many_to_one so that this check is done while merging and will throw an error if the assumption is not met. For more...

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Modern Time Series Forecasting with Python
Published in: Nov 2022Publisher: PacktISBN-13: 9781803246802

Author (1)

author image
Manu Joseph

Manu Joseph is a self-made data scientist with more than a decade of experience working with many Fortune 500 companies enabling digital and AI transformations, specifically in machine learning-based demand forecasting. He is considered an expert, thought leader, and strong voice in the world of time series forecasting. Currently, Manu leads applied research at Thoucentric, where he advances research by bringing cutting-edge AI technologies to the industry. He is also an active open-source contributor and developed an open-source library—PyTorch Tabular—which makes deep learning for tabular data easy and accessible. Originally from Thiruvananthapuram, India, Manu currently resides in Bengaluru, India, with his wife and son
Read more about Manu Joseph