Multiple Linear Regression with Kalman Filter for Predicting End Prices of Online Auctions

Li, Xiaohui; Dong, Hongbin; Han, Shuang

doi:10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00042

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…According to Mahoto et al, their model helps the customer purchase the desired product at an affordable price while the business accomplishes its goals by selling out the maximum number of products at a certain time and keeping its profit stable. On the other hand, the authors in [ 64 ] showcased a methodology for predicting auction end prices, which has the main aim of maximizing the profit of an e-commerce online auction platform. This proposed framework utilizes a fusion algorithm that leads to a more effective outcome and manages to overcome the shortcomings of the simple multiple LR algorithm.…”

Section: Resultsmentioning

confidence: 99%

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Daskalakis¹,

Remoundou²,

Πεππές³

et al. 2022

Sensors

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The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper’s goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.

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Section: Resultsmentioning

confidence: 99%

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Daskalakis¹,

Remoundou²,

Πεππές³

et al. 2022

Sensors

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“…In many cases, the possibility of predicting the final price for the goods was analyzed. The studies below sometimes took into account all available variables (Li, Dong, Han, 2020;Khadge, Kulkarni, 2016;Yokotani, Huang, Kawagoe, 2012), but often the focus was on a specific factor or their group. These factors include: the bidder's experience (taking into account such issues as the tendency to submit multiple bids (Borle, Boatwright, Kadane, 2006), the issue of overestimating the final price , hedonic motivations (Cui, Lai, Lowry, Lei, 2020), researching multiple categories of goods (Srinivasan, Wang, 2010) or groups of selected goods (Chow, 2019) -a separate problem was, for example, the possibility of grouping goods of different categories against different characteristics (Li, 2012)); the method of describing and promoting the auctions (Tsai, Huang, 2011); information retrieval cost (Haruvy, Popkowski-Leszczyc, 2010); behavioral models (who, when and how many bids throughout the bid sequence (Park, Bradlow, 2005)); seller's reputation (taking into account total turnover (Hayne, Wang, Mendonca, 2012) or the accumulation of bad ratings by buyers (Canals-Cerdá, 2012).…”

Section: The State Of Researchmentioning

confidence: 99%

Online Auctions End Time and its Impact on Sales Success – Analysis of the Odds Ratio on a Selected Central European Market

Zakonnik

Czerwonka

Zajdel

2022

Folia Oeconomica Stetinensia

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Research background: E-commerce is developing rapidly, especially during the Covid19 pandemic. This fact can benefit individuals who want to sell their already used goods. Importantly, for sellers, it is not always a priority to get the highest price, but sometimes it is simply effective to get rid of the goods at a satisfactory price. Purpose: The aim of this article is to analyze the impact of the broadly understood time of the end of the online auction on the success or failure of a sale. Research methodology: In the study, the raw odds ratio was used for the effect of a single variable. Next, the impact of specific variables within the set of risk factors was determined using the logistic regression. Results: Auctions ending in the evening were found to be more than 150% more likely to be successful, while night hours reduced the chance of success by 50%. The day’s most favorable for sales are Monday and Tuesday, the opposite pattern was observed for Wednesday, Thursday and Friday. An interesting relationship was found for the second half of the month, which increased the possibility of selling the goods by over 20%. Novelty: In the literature there are almost none that would focus on the analysis of the possibility of ending the auction with a sale (i.e. success) in the context of the auction end time on the Central European market. This issue is usually discussed on the side and has not been analyzed comprehensively – this paper is a step forward in this direction.

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“…Among them, the Kalman filter represents a very appealing choice [29][30][31]. As compared to other adaptive filters, where the system to be identified is considered to be deterministic in their derivations, the Kalman filter takes the "uncertainties" in the system into account, and is thus successfully employed in a wide range of applications, e.g., [32][33][34][35][36][37] and the references therein. Recently, in [36], an adaptive Kalman-filter-based variational Bayesian, which achieves a simultaneous estimation of the process noise covariance matrix and of the measurement noise covariance matrix, is presented, with applications in target tracking.…”

Section: Introductionmentioning

confidence: 99%

A Kalman Filter for Multilinear Forms and Its Connection with Tensorial Adaptive Filters

Dogariu

Paleologu

Benesty

et al. 2021

Sensors

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The Kalman filter represents a very popular signal processing tool, with a wide range of applications within many fields. Following a Bayesian framework, the Kalman filter recursively provides an optimal estimate of a set of unknown variables based on a set of noisy observations. Therefore, it fits system identification problems very well. Nevertheless, such scenarios become more challenging (in terms of the convergence and accuracy of the solution) when the parameter space becomes larger. In this context, the identification of linearly separable systems can be efficiently addressed by exploiting tensor-based decomposition techniques. Such multilinear forms can be modeled as rank-1 tensors, while the final solution is obtained by solving and combining low-dimension system identification problems related to the individual components of the tensor. Recently, the identification of multilinear forms was addressed based on the Wiener filter and most well-known adaptive algorithms. In this work, we propose a tensorial Kalman filter tailored to the identification of multilinear forms. Furthermore, we also show the connection between the proposed algorithm and other tensor-based adaptive filters. Simulation results support the theoretical findings and show the appealing performance features of the proposed Kalman filter for multilinear forms.

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Multiple Linear Regression with Kalman Filter for Predicting End Prices of Online Auctions

Cited by 4 publications

References 37 publications

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Online Auctions End Time and its Impact on Sales Success – Analysis of the Odds Ratio on a Selected Central European Market

A Kalman Filter for Multilinear Forms and Its Connection with Tensorial Adaptive Filters

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