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https://ieeexplore.ieee.org/document/4344520/
Aug 27, 2007 · Abstract: Demand forecasting plays a crucial role for supply chain management of retail industry. The future demand for a certain product constructs the basis of its relevant replenishment system. In this research, the technique of support vector machine (SVM) is …
https://www.researchgate.net/publication/4279693_Demand_Forecasting_by_Using_Support_Vector_Machine
The paper in [7] employs the technique of support vector machine (SVM) for demand forecasting. Various factors that affect the product demand such as seasonal and promotional factors have been ...
https://www.sciencedirect.com/science/article/pii/S1877705817314200
Support Vector Machines in Urban Water Demand Forecasting Using Phase Space ... applications of machine learning techniques are yet to be explored in detail. This research proposes a support vector machine (SVM) model, using polynomial kernel function to forecast monthly water demand of City of Kelowna (CKD), Canada. ... Conventional methods of ...Cited by: 10
https://www.sciencedirect.com/science/article/pii/S0360835218301864
Given a set of candidate models, rather than considering any individual criterion, a support vector machine (SVM) is trained at each forecasting origin to select the best model using all this information. The effects of this approach are explored for the 229 stock keeping units (SKUs) of a leading household and personal care manufacturer in the UK.Cited by: 4
https://www.researchgate.net/publication/241732545_Demand_forecasting_of_perishable_farm_products_using_support_vector_machine
Demand forecasting of perishable farm products using support vector machine Article (PDF Available) in International Journal of Systems Science 44(3):1-12 · January 2011 with 1,288 Reads
https://arxiv.org/pdf/0705.0969
Artificial Neural Networks and Support Vector Machines for Water Demand Time Series Forecasting Ishmael S. Msiza, Fulufhelo V. Nelwamondo and Tshilidzi Marwala Abstract – Water plays a pivotal role in many physical processes, and most importantly in sustaining human life, animal life and plant life.
https://www.csie.ntu.edu.tw/~cjlin/papers/elf.pdf
Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 Bo-Juen Chen, Ming-Wei Chang, and Chih-Jen Lin ... a support vector machine (SVM) model, which was the winning ... However, while forecasting load demand, more …
https://arxiv.org/pdf/0705.0969
Artificial Neural Networks and Support Vector Machines for Water Demand Time Series Forecasting Ishmael S. Msiza, Fulufhelo V. Nelwamondo and Tshilidzi Marwala Abstract – Water plays a pivotal role in many physical processes, and most importantly in …
https://www.hindawi.com/journals/complexity/2019/9067367/
Demand forecasting is one of the main issues of supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. In this work, an intelligent demand forecasting ...
https://link.springer.com/chapter/10.1007/978-3-030-15032-7_1
Mar 15, 2019 · To forecast electric load, we have applied Support Vector Machine (SVM) set tuned with three super parameters, i.e., kernel parameter, cost penalty, and incentive loss function parameter. Electricity market data is used in our proposed model. Weekly and months ahead forecasting experiments are conducted by proposed model.
http://adsabs.harvard.edu/abs/2013IJSyS..44..556D
Demand forecasting of perishable farm products using support vector machine: Authors: Du, Xiao Fang; Leung, Stephen C. H.; ... This article presents a new algorithm for forecasting demand for perishable farm products, based on the support vector machine (SVM) method. Since SVMs have greater generalisation performance and guarantee global minima ...
https://ieeexplore.ieee.org/document/1350819/
In 2001, EUNITE network organized a competition aiming at mid-term load forecasting (predicting daily maximum load of the next 31 days). During the competition we proposed a support vector machine (SVM) model, which was the winning entry, to solve the problem.
https://pythonprogramming.net/forecasting-predicting-machine-learning-tutorial/
Welcome to part 5 of the Machine Learning with Python tutorial series, currently covering regression. Leading up to this point, we have collected data, modified it a bit, trained a classifier and even tested that classifier. In this part, we're going to use our classifier to actually do some forecasting for us!
https://dl.acm.org/citation.cfm?id=3033517
Forecasting Demand in Supply Chain Using Machine Learning Algorithms. Author: ... sources of information and the power of advanced machine learning algorithms for lowering the uncertainty barrier in forecasting supply chain demand. AUTHORS ... Nonlinear prediction of chaotic time series using support vector machines. Proceedings of the 1997 ...
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000004344520
Demand forecasting plays a crucial role for supply chain management of retail industry. The future demand for a certain product constructs the basis of its relevant replenishment system. In this research, the technique of support vector machine (SVM) is employed for demand forecasting. Various factors that affect the product demand such as seasonal and promotional factors have been taken into ...
https://medium.com/alloytech/choosing-the-right-demand-forecasting-model-d8a8b4c6878c
Nov 28, 2018 · Choosing the “right” demand forecasting model. ... Products with well-defined seasonality or changes in demand, ... CNN) Support Vector Machine. Best for: ...
https://academiccommons.columbia.edu/doi/10.7916/D85D90X7
This model uses Support Vector Machine Regression (SVMR), a method that builds a regression based purely on historical data of the building, requiring no knowledge of its size, heating and cooling methods, or any other physical properties. ... Forecasting Energy Demand in Large Commercial Buildings Using Support Vector Machine Regression ...
https://www.hindawi.com/journals/jat/2018/3189238/
Support Vector Machine Partial Online Model. Support vector machine partial online (SVMPOL) model is also based on the theory of SVM, to extract input features, to train the real-time updated testing data, to use intelligent algorithm, to find the optimal parameters, and to get real-time prediction function to realize the short-term forecasting.
https://www.semanticscholar.org/paper/Consumer-Product-Demand-Forecasting-based-on-Neural-Kandananond/deb388e5ede18eeb3d3753f3d288ec8b2b75426c
The nature of consumer products causes the difficulty in forecasting the future demands and the accuracy of the forecasts significantly affects the overall performance of the supply chain system. In this study, two data mining methods, artificial neural network (ANN) and support vector machine (SVM), were utilized to predict the demand of consumer products. The training data used was the ...
https://link.springer.com/chapter/10.1007/11596448_75
Support vector machines (SVMs) have been successfully applied to solve nonlinear regression and times series problems. However, the application of SVMs for tourist forecasting has not been widely... Forecasting Tourism Demand Using a Multifactor Support Vector Machine Model SpringerLink
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