An Approach to Human Resource Demand Forecasting Based on Machine Learning Techniques

2021 ◽  
pp. 389-396
Author(s):  
Kim-Son Nguyen ◽  
Ho-Dac Hung ◽  
Van-Tai Tran ◽  
Tuan-Anh Le
Keyword(s):  
Machine Learning ◽  
Human Resource ◽  
Demand Forecasting ◽  
Machine Learning Techniques ◽  
Resource Demand ◽  
Learning Techniques

Application of Machine Learning Techniques in Railway Demand Forecasting

2021 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Miguel F. Anjos ◽  
Neda Etebarialamdari ◽  
Gilles Savard
Keyword(s):  
Machine Learning ◽  
Demand Forecasting ◽  
Machine Learning Techniques ◽  
Learning Techniques

Application of machine learning techniques for supply chain demand forecasting

2008 ◽  
Vol 184 (3) ◽  
pp. 1140-1154 ◽  
Author(s):  
Real Carbonneau ◽  
Kevin Laframboise ◽  
Rustam Vahidov
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Demand Forecasting ◽  
Machine Learning Techniques ◽  
Learning Techniques

Data-driven short-term natural gas demand forecasting with machine learning techniques

2021 ◽  
pp. 108979
Author(s):  
Vinayak Sharma ◽  
Ümit Cali ◽  
Bhav Sardana ◽  
Murat Kuzlu ◽  
Dishant Banga ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Gas ◽  
Demand Forecasting ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Learning Techniques ◽  
Natural Gas Demand

scholarly journals Short-term water demand forecasting using machine learning techniques

2018 ◽  
Vol 20 (6) ◽  
pp. 1343-1366 ◽  
Author(s):  
A. Antunes ◽  
A. Andrade-Campos ◽  
A. Sardinha-Lourenço ◽  
M. S. Oliveira
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Demand Forecasting ◽  
Maximum Level ◽  
Water Utilities ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Water Demand Forecasting ◽  
Learning Techniques

Abstract Nowadays, a large number of water utilities still manage their operation on the instant water demand of the network, meaning that the use of the equipment is conditioned by the immediate water necessity. The water reservoirs of the networks are filled using pumps that start working when the water level reaches a specified minimum, stopping when it reaches a maximum level. Shifting the focus to water management based on future demand allows use of the equipment when energy is cheaper, taking advantage of the electricity tariff in action, thus bringing significant financial savings over time. Short-term water demand forecasting is a crucial step to support decision making regarding the equipment operation management. For this purpose, forecasting methodologies are analyzed and implemented. Several machine learning methods, such as neural networks, random forests, support vector machines and k-nearest neighbors, are evaluated using real data from two Portuguese water utilities. Moreover, the influence of factors such as weather, seasonality, amount of data used in training and forecast window is also analysed. A weighted parallel strategy that gathers the advantages of the different machine learning techniques is suggested. The results are validated and compared with those achieved by autoregressive integrated moving average (ARIMA) also using benchmarks.

Application of machine learning techniques in railway demand forecasting

2021 ◽  
Vol 12 (1/2) ◽  
pp. 132
Author(s):  
Neda Etebari Alamdari ◽  
Miguel F. Anjos ◽  
Gilles Savard
Keyword(s):  
Machine Learning ◽  
Demand Forecasting ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

2019 ◽  
Author(s):  
Rodolfo Garcia Flores ◽  
soumya banerjee ◽  
George Mathews
Keyword(s):  
Machine Learning ◽  
Demand Forecasting ◽  
Operating Conditions ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Economic Returns ◽  
Competitive Edge ◽  
Learning Techniques ◽  
Infrastructure Investments ◽  
To Come

When stakeholders commit to building infrastructure as part of strategic, long-term planning, the final facilities are not normally amenable to modification after completion. A consequence of this is that users are forced to operatewithin the original specifications for, at least, as long as it takes to carry out major refurbishments or retrofitting, and even then, the constraints imposed by the original layout may be inescapable.On one hand, the original infrastructure plans enhance (or limit) the users' ability to operate efficiently for years to come. As time passes and the payback period approaches, changing operating conditions and unforeseen bottlenecks in the original blueprint can, at best, affect the economic returns and, at worst, defeat the purpose of the whole project (see, for example, Castellon airport in Spain, which was built but is grossly underutilised), producing unanticipated economical, social and political repercussions. On the other hand, managers and operators (that is, those living with the consequences of the strategic planning) have some leeway to compensate for miscalculations by means of their tacticaland operational planning.In this chapter, we explore the use of quantitative techniques to, first, amend bottlenecks and uncertain market and operating conditions that affect the performance of infrastructure investments (the tactic and operational levels), and second, validate the effectiveness of the original infrastructure design (the strategic level) under these changing conditions.More specifically, we present a rail scheduling case study where we combine demand forecasting using Machine Learning techniques and formal Operations Research methods to assess and maximise the value of already-existing infrastructure.Rail scheduling is a typical optimisation problem popular in the literature, but its potential value is bounded not only by its technical properties and specifications (how good the algorithm is) but also by the accuracy of data feeding the algorithm. Such data is critical in specifying the demand thata facility will experience in the future, and the costs that will be incurred to operate it. The use of intensive data analytics and appropriate Machine Learning techniques can resolve this and provide a substantial competitive edge forinvestors and operators of rail inter-modal terminals.We anticipate that Machine Learning algorithms that predict future demand, coupled with optimisation techniques that streamline operations of facilities, can be integrated to create tools that help policy makers and terminal operators maximise the value of their current infrastructure, while meeting ever-changing demand.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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