A Data-Driven Approach to the Fragile Families Challenge: Prediction through Principal-Components Analysis and Random Forests

Compton, Ryan

doi:10.1177/2378023118818720

Cited by 8 publications

(11 citation statements)

References 11 publications

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“…Furthermore, among the collected data, not all factors have a high correlation with the target of the forecast. erefore, before data analysis, database normalization and dimensionality reduction with principal component analysis (PCA) [38][39][40] are required to retain the eigenvalues with higher correlations to the target of the forecast, take the eigenvalues as the variables, and obtain the dependent variable for sales forecasting.…”

Section: Data Collection and Preprocessmentioning

confidence: 99%

Deep Learning Based Purchase Forecasting for Food Producer-Retailer Team Merchandising

Kao

Chueh

2022

Scientific Programming

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Expired foods turning into waste has always been an important issue. In Taiwan, more than 36,000 metric tons of unopened expired food, worth more than $130 million, are thrown away from retail stores as waste each year. Insufficient inventory results in the loss of business prospects for retailers, whilst excessive inventory results in abandoned merchandise. Foods with a short shelf life are particularly vulnerable. Typically, food producer and retailer would form team merchandising (MD). The team MD mechanism is responsible for ensuring safety and quality, not for forecasting demand. This study uses artificial neural networks (ANNs) to analyze sales data to forecasting purchase volume in response to changes in store environment, weather, events, and consumer attributes. The study object is a sort of cream puff with a short shelf life cobranded by a retailer. According to the experimental results, the adopted proposed model in this study effectively reduces the error in purchasing; the mean-square percentage error (MSPE) of the forecast values is less than 6%. The importance of this study is on promoting the team MD’s green energy management capabilities in food production and verifiably achieving the goal of environmental sustainability.

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Section: Data Collection and Preprocessmentioning

confidence: 99%

Deep Learning Based Purchase Forecasting for Food Producer-Retailer Team Merchandising

Kao

Chueh

2022

Scientific Programming

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“…These new version sometimes introduce what are called breaking changes, which cause code that used to work to suddenly break. For example, the improvements between Python 2 and Python 3 introduced some breaking changes into Compton (2019), which we discovered when we attempted to use Python 3 to import preprocessed data files that had been serialized with Python 2. Furthermore, new versions sometimes cause much more subtle errors.…”

Section: Five-item Checklistmentioning

confidence: 99%

Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge

Liu

Salganik

2019

Socius

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Reproducibility is fundamental to science, and an important component of reproducibility is computational reproducibility: the ability of a researcher to recreate the results of a published study using the original author’s raw data and code. Although most people agree that computational reproducibility is important, it is still difficult to achieve in practice. In this article, the authors describe their approach to enabling computational reproducibility for the 12 articles in this special issue of Socius about the Fragile Families Challenge. The approach draws on two tools commonly used by professional software engineers but not widely used by academic researchers: software containers (e.g., Docker) and cloud computing (e.g., Amazon Web Services). These tools made it possible to standardize the computing environment around each submission, which will ease computational reproducibility both today and in the future. Drawing on their successes and struggles, the authors conclude with recommendations to researchers and journals.

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“…These new version sometimes introduce what are called breaking changes, which cause code that used to work to suddenly break. For example, the improvements between Python 2 and Python 3 introduced some breaking changes into Compton (2019), which we discovered when we attempted to use Python 3 to import pre-processed data files that had been serialized with Python 2. Further, new versions sometimes cause much more subtle errors.…”

Section: Requirements Filesmentioning

confidence: 99%

Successes and struggles with computational reproducibility: Lessons from the Fragile Families Challenge

Liu¹,

Salganik²

2019

Preprint

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Reproducibility is fundamental to science, and an important component of reproducibility is computational reproducibility: the ability of a researcher to recreate the results in a published paper using the original author's raw data and code. Although most people agree that computational reproducibility is important, it is still difficult to achieve in practice. In this paper, we describe our approach to enabling computational reproducibility for the 12 papers in this special issue of Socius about the Fragile Families Challenge. Our approach draws on two tools commonly used by professional software engineers but not widely used by academic researchers: software containers (e.g., Docker) and cloud computing (e.g., Amazon Web Services). These tools enabled us to standardize the computing environment around each submission, which will ease computational reproducibility both today and in the future. Drawing on our successes and struggles, we conclude with recommendations to authors and journals.

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A Data-Driven Approach to the Fragile Families Challenge: Prediction through Principal-Components Analysis and Random Forests

Cited by 8 publications

References 11 publications

Deep Learning Based Purchase Forecasting for Food Producer-Retailer Team Merchandising

Deep Learning Based Purchase Forecasting for Food Producer-Retailer Team Merchandising

Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge

Successes and struggles with computational reproducibility: Lessons from the Fragile Families Challenge

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