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Intermittent Demand Planning with Tree Random Forest and Extrapolation
(Springer Nature Switzerland, 2025)
Rosso Pelayo, Dafne Angelica
Intermittent demand planning is one of the most challenging tasks in supply chain management. Planning inventory at the point of sale often involves dealing with discontinuous historical data. This discontinuous demand refers to demand that is sporadic and does not follow a consistent pattern. Various factors, such as seasonal changes, market disruptions, or unique customer behavior, can contribute to this irregularity. Planning for discontinuous forecasting involves managing irregular or unpredictable demand patterns, which can be challenging for businesses. In this paper, we propose a machine learning approach to address this problem. This approach was successfully implemented approximately a year ago, resulting in up to a 50% improvement in demand estimation in certain scenarios. This significant enhancement has greatly minimized the frequency of unfulfilled customer requests and improving overall customer satisfaction. Our model consists of a two-phase algorithm. First, we apply random forest as a regression method. Second, we obtain the expected discontinuous series by extrapolating the results of the random forest model. In mathematics, extrapolation is used to estimate the value of a quantity outside the interval in which that quantity is known. ©The author ©Springer.
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A machine learning-based analytical intelligence system for forecasting demand of new products based on chlorophyll : a hybrid approach
(Springer, 2024)
Rodríguez Aguilar, Román
;
Marmolejo Saucedo, José Antonio
;
Garcia-Llamas, Eduardo
;
Rodríguez-Aguilar, Miriam
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Marmolejo-Saucedo, Liliana
This manuscript addresses the problem of forecasting the demand for innovative products with limited and inhomogeneous sales data over time. The main objective of the study is to use the information available from a group of innovative chlorophyll-based food products to build a coherent demand forecasting system. From a transactional database, time series were constructed for each group of products, analyzing the stationarity and seasonality of the time series through the Dickey–Fuller and Canova–Hansen tests. Likewise, an ARIMA model, a long short-term memory (LSTM) recurrent deep neural network, and a support vector machine (SVM) were trained to select the best model for each product based on a forecast performance metric. A comparison between classical forecasting techniques and machine learning models is shown. The LSTM neural network was the best model for most products because the internal architecture of the network allows not only to capture non-linear relationships between variables but is also capable of controlling the flow of information to preserve characteristics over time that are relevant for forecasts. The second-best model was the SVM, which allows capturing non-linear behaviors through kernel functions and uses a smaller amount of data for its estimation. Finally, the ARIMA model presented the lowest performance for all products. The objective of having various methodologies is that the system allows the best forecast to be selected according to the type of product, availability of information and methodology used, which will allow the company to integrate new products into the system over time. ©Springer
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Ventilator Pressure Prediction Using a Regularized Regression Model
(2022)
Arellano, Amaury
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Bustamante, Erick
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Garza, Carlos
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Ponce, Hiram
The mechanical ventilation is one of the most frequent methods used in Intensive Care Units (ICUs) to improve the breathing of patients. During the early days of the COVID-19 pandemic, the use of mechanical ventilators has been crucial. In this work, we propose to build a Lasso regression model based on lung simulators for predicting the airway pressure in the respiratory circuit of ventilators while breathing. We model the whole breathing process in two separate states. After that, we analyze the feature importance in the regression model to better understand the ventilator pressure prediction. We anticipate this model would help improving the patient’s health and overcoming the cost barrier of new methods for mechanical ventilators. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Scopus© Citations 2 18 1
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A Survey on Freezing of Gait Detection and Prediction in Parkinson’s Disease
(2020)
Martinez-Villaseñor, Lourdes
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Ponce, Hiram
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Miralles-Pechuán, Luis
Most of Parkinson’s disease (PD) patients present a set of motor and non-motor symptoms and behaviors that vary during the day and from day-to-day. In particular, freezing of gait (FOG) impairs their quality of life and increases the risk of falling. Smart technology like mobile communication and wearable sensors can be used for detection and prediction of FOG, increasing the understanding of the complex PD. There are surveys reviewing works on Parkinson and/or technologies used to manage this disease. In this review, we summarize and analyze works addressing FOG detection and prediction based on wearable sensors, vision and other devices. We aim to identify trends, challenges and opportunities in the development of FOG detection and prediction systems. © 2020, Springer Nature Switzerland AG.
Scopus© Citations 1 17 1

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