Analytics

16 pages, 2475 KiB

Open AccessArticle

Prioritizing Cell Tower Site Recommendations outside U.S. Metropolitan Areas

by Kurt Pflughoeft, Grace Nemecek and Nikolaus T. Butz

Analytics 2022, 1(1), 56-71; https://doi.org/10.3390/analytics1010006 - 08 Sep 2022

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Cell phone technology has advanced rapidly with the start of 5G being rolled out across the networks. To keep up with this demand, cell tower companies have responded by erecting numerous towers. Engineers and researchers analyze the network topography to make recommendations for cell tower locations. Cell tower companies evaluate these recommendations using a host of other factors. In this research, a model was developed to help a regional telecommunications company predict throughput for locations using competitive and demand factors. Model results represented a large improvement over internal key performance indicators. Full article

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2 pages, 325 KiB

Open AccessEditorial

Analytics—Systematic Computational Analysis of Data

by Jesus S. Aguilar-Ruiz

Analytics 2022, 1(1), 54-55; https://doi.org/10.3390/analytics1010005 - 31 Aug 2022

Viewed by 2704

Abstract

Since the envisioning of the concept of Artificial Intelligence in the 1950s, the interest in making machines emulate human behavior has increased, scientific dedication has grown, and, consequently, new concepts have appeared, with unequal success [...] Full article

19 pages, 1477 KiB

Open AccessArticle

Financial Vision-Based Reinforcement Learning Trading Strategy

by Yun-Cheng Tsai, Fu-Min Szu, Jun-Hao Chen and Samuel Yen-Chi Chen

Analytics 2022, 1(1), 35-53; https://doi.org/10.3390/analytics1010004 - 09 Aug 2022

Cited by 3 | Viewed by 2078

Abstract

Recent advances in artificial intelligence (AI) for quantitative trading have led to its general superhuman performance among notable trading performance results. However, if we use AI without proper supervision, it can lead to wrong choices and huge losses. Therefore, we need to ask why AI makes decisions and how AI makes decisions so that people can trust AI. By understanding the decision process, people can make error corrections, so the need for explainability highlights the artificial intelligence challenges that intelligent technology can explain in trading. This research focuses on financial vision, an explainable approach, and the link to its programmatic implementation. We hope our paper can refer to superhuman performance and the reasons for decisions in trading systems. Full article

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8 pages, 274 KiB

Open AccessArticle

General Equilibrium with Price Adjustments—A Dynamic Programming Approach

by Jussi Lindgren

Analytics 2022, 1(1), 27-34; https://doi.org/10.3390/analytics1010003 - 06 Jul 2022

Viewed by 3017

Abstract

This research article develops a dynamic framework for the Walrasian pure exchange economy and thus extends the static Walrasian general equilibrium theory into a dynamic one with price adjustments. An evolution equation for the price vector is derived from dynamic programming considerations. The economy tries to move from disequilibrium to general equilibrium by minimizing certain cost functional. The cost functional measures transactions costs and the total expenditure of agents when they optimize individually. Price determination is directly related to a gradient search. The general equilibrium is shown to be stable in the sense of Lyapunov if price adjustments can be large, when needed. The conditional stability could be one reason for volatility clustering in financial time series. Full article

12 pages, 531 KiB

Open AccessArticle

A New Semiparametric Regression Framework for Analyzing Non-Linear Data

by Wesley Bertoli, Ricardo P. Oliveira and Jorge A. Achcar

Analytics 2022, 1(1), 15-26; https://doi.org/10.3390/analytics1010002 - 16 Jun 2022

Cited by 1 | Viewed by 1810

Abstract

This work introduces a straightforward framework for semiparametric non-linear models as an alternative to existing non-linear parametric models, whose interpretation primarily depends on biological or physical aspects that are not always available in every practical situation. The proposed methodology does not require intensive numerical methods to obtain estimates in non-linear contexts, which is attractive as such algorithms’ convergence strongly depends on assigning good initial values. Moreover, the proposed structure can be compared with standard polynomial approximations often used for explaining non-linear data behaviors. Approximate posterior inferences for the semiparametric model parameters were obtained from a fully Bayesian approach based on the Metropolis-within-Gibbs algorithm. The proposed structures were considered to analyze artificial and real datasets. Our results indicated that the semiparametric models outperform linear polynomial regression approximations to predict the behavior of response variables in non-linear settings. Full article

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14 pages, 837 KiB

Open AccessArticle

Analytics Capability and Firm Performance in Supply Chain Organizations: The Role of Employees’ Analytics Skills

by Samira Farivar, Amirmohsen Golmohammadi and Alejandro Ramirez

Analytics 2022, 1(1), 1-14; https://doi.org/10.3390/analytics1010001 - 12 May 2022

Viewed by 2329

Abstract

Developing analytics capability has become one of the main priorities in organizations today. Despite the increasing use of analytics, the necessary conditions to obtain the expected benefits from such investment still need to be examined. Relying on information processing theory (OIPT), this study sheds some light on the requirements for properly utilizing analytics to receive the potential benefits in supply chain firms. Specifically, we study the role of supply chain process integration in developing analytics capability, and we further examine the role of analytics capability and employees’ analytics skills in improving firm performance. Survey data collected from 240 supply chain top- and middle-level managers show that supply chain process integration enhances firms’ analytics capability. However, analytics capability alone is not sufficient in improving firm performance; it must be complemented with employees’ analytics skills. These findings extend the current literature on supply chain analytics and provide guidance and insights to supply chain managers for their analytics capability investments. Full article

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Analytics, Volume 1, Issue 1 (September 2022) – 6 articles

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