Markov chain based modelling and prediction of natural gas allocation structure in Pakistan

2020 ◽  
Vol 14 (5) ◽  
pp. 911-933
Author(s):  
Hussaan Ahmad ◽  
Nasir Hayat
Keyword(s):  
Markov Chain ◽  
Natural Gas ◽  
Polynomial Regression ◽  
Markov Chain Model ◽  
Chain Model ◽  
Transition Matrices ◽  
Allocation Pattern ◽  
Content Type ◽  
Structural Prediction ◽  
Order Polynomial

Purpose The purpose of this paper is to analyze the historical gas allocation pattern for seeking appropriate arrangement and utilization of potentially insufficient natural gas supply available in Pakistan up to 2030. Design/methodology/approach This study presents Markov chain-based modeling of historical gas allocation data followed by its validation through error evaluation. Structural prediction using classical Chapman–Kolmogorov method and varying-order polynomial regression in the historical transition matrices are presented. Findings Markov chain model reproduces the terminal state vector with 99.8 per cent accuracy, thus demonstrating its validity for capturing the history. Lower order polynomial regression results in better structural prediction compared with higher order ones in terms of closeness with Markov approach-based prediction. Research limitations/implications The data belongs to a certain geographic region with specific gas demand and supply profile. The proposition may be tested further by researchers to check the validity for other comparable structural predictions/analyses. Practical implications This study can facilitate policy-making in the field of natural gas allocation and management in Pakistan specifically and other comparable countries generally. Originality/value Two major literature gaps filled through this study are: first, Markov chain model becomes stationary when projected using Chapman–Kolmogorov relation in terms of a fixed, average transition matrix resulting in an equilibrium state after a finite number of future steps. Second, most of the previous studies analyze various gas consumption sectors individually, thus lacking integrated gas allocation policy.

A New Method for Modeling the Behavior of Finite Population Evolutionary Algorithms

2010 ◽  
Vol 18 (3) ◽  
pp. 451-489 ◽  
Author(s):  
Tatsuya Motoki
Keyword(s):  
Markov Chain ◽  
Evolutionary Algorithms ◽  
Finite Population ◽  
Fitness Landscape ◽  
Markov Chain Model ◽  
Search Space ◽  
Chain Model ◽  
Transition Matrices ◽  
Memory Space ◽  
Exact Model

As practitioners we are interested in the likelihood of the population containing a copy of the optimum. The dynamic systems approach, however, does not help us to calculate that quantity. Markov chain analysis can be used in principle to calculate the quantity. However, since the associated transition matrices are enormous even for modest problems, it follows that in practice these calculations are usually computationally infeasible. Therefore, some improvements on this situation are desirable. In this paper, we present a method for modeling the behavior of finite population evolutionary algorithms (EAs), and show that if the population size is greater than 1 and much less than the cardinality of the search space, the resulting exact model requires considerably less memory space for theoretically running the stochastic search process of the original EA than the Nix and Vose-style Markov chain model. We also present some approximate models that use still less memory space than the exact model. Furthermore, based on our models, we examine the selection pressure by fitness-proportionate selection, and observe that on average over all population trajectories, there is no such strong bias toward selecting the higher fitness individuals as the fitness landscape suggests.

Risk assessment for financial accounting: modeling probability of default

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tobias Filusch
Keyword(s):  
Markov Chain ◽  
Business Cycle ◽  
Markov Chain Model ◽  
Accounting Standards ◽  
Financial Accounting ◽  
Homogeneous Markov Chain ◽  
Chain Model ◽  
Probability Of Default ◽  
Content Type ◽  
Term Structures

Purpose This paper aims to introduce and tests models for point-in-time probability of default (PD) term structures as required by international accounting standards. Corresponding accounting standards prescribe that expected credit losses (ECLs) be recognized for the impairment of financial instruments, for which the probability of default strongly embodies the included default risk. This paper fills the research gap resulting from a lack of models that expand upon existing risk management techniques, link PD term structures of different risk classes and are compliant with accounting standards, e.g. offering the flexibility for business cycle-related variations. Design/methodology/approach The author modifies the non-homogeneous continuous-time Markov chain model (NHCTMCM) by Bluhm and Overbeck (2007a, 2007b) and introduces the generalized through-the-cycle model (GTTCM), which generalizes the homogeneous Markov chain approach to a point-in-time model. As part of the overall ECL estimation, an empirical study using Standard and Poor’s (S&P) transition data compares the performance of these models using the mean squared error. Findings The models can reflect observed PD term structures associated with different time periods. The modified NHCTMCM performs best at the expense of higher complexity and only its cumulative PD term structures can be transferred to valid ECL-relevant unconditional PD term structures. For direct calibration to these unconditional PD term structures, the GTTCM is only slightly worse. Moreover, it requires only half of the number of parameters that its competitor does. Both models are useful additions to the implementation of accounting regulations. Research limitations/implications The tests are only carried out for 15-year samples within a 35-year span of available S&P transition data. Furthermore, a point-in-time forecast of the PD term structure requires a link to the business cycle, which seems difficult to find, but is in principle necessary corresponding to the accounting requirements. Practical implications Research findings are useful for practitioners, who apply and develop the ECL models of financial accounting. Originality/value The innovative models expand upon the existing methodologies for assessing financial risks, motivated by the practical requirements of new financial accounting standards.

scholarly journals Will this search end up with booking? Modeling airline booking conversion of anonymous visitors

2020 ◽  
Vol 27 (2) ◽  
pp. 237-250
Author(s):  
Misuk Lee
Keyword(s):  
Markov Chain ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Distribution Channel ◽  
Stochastic Approach ◽  
Tourism Industry ◽  
Chain Model ◽  
Online Search ◽  
Clickstream Data ◽  
Content Type

Purpose Over the past two decades, online booking has become a predominant distribution channel of tourism products. As online sales have become more important, understanding booking conversion behavior remains a critical topic in the tourism industry. The purpose of this study is to model airline search and booking activities of anonymous visitors. Design/methodology/approach This study proposes a stochastic approach to explicitly model dynamics of airline customers’ search, revisit and booking activities. A Markov chain model simultaneously captures transition probabilities and the timing of search, revisit and booking decisions. The suggested model is demonstrated on clickstream data from an airline booking website. Findings Empirical results show that low prices (captured as discount rates) lead to not only booking propensities but also overall stickiness to a website, increasing search and revisit probabilities. From the decision timing of search and revisit activities, the author observes customers’ learning effect on browsing time and heterogeneous intentions of website visits. Originality/value This study presents both theoretical and managerial implications of online search and booking behavior for airline and tourism marketing. The dynamic Markov chain model provides a systematic framework to predict online search, revisit and booking conversion and the time of the online activities.

The forecast of cultivate land quantity based on Grey-Markov model

2015 ◽  
Vol 5 (1) ◽  
pp. 127-136 ◽  
Author(s):  
Hongyan Huan ◽  
Qing-mei Tan
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Land Use Planning ◽  
Markov Chain Model ◽  
Arable Land ◽  
Jiangsu Province ◽  
Chain Model ◽  
Cultivated Land ◽  
Content Type ◽  
Investigation Methods

Purpose – The purpose of this paper is to employ the Grey-Markov Chain Model for the scale prediction of cultivated land and took an empirical research with the case of Jiangsu province. Design/methodology/approach – Along with China’s industrialization and urbanization accelerated, a large number of cultivated land converse into construction land. The change of utilization of cultivated land concerns national food security and sustainable development of economy and society. Due to the fact that the different investigation methods of arable land usually cause a uncertain. The Grey-Markov model combines the Grey GM(1,1) and Markov chain, with two advantages of dealing with poor information and long-term and volatile series. A numeric example of scale prediction of cultivated land in Jiangsu province is also computed in the third part of the paper. Findings – The results show that the Grey-Markov Chain Model has a higher prediction accuracy compared with GM (1,1), which is a reliable guarantee for the change of cultivated land resources. Practical implications – The forecast of cultivated land can provide useful information for the general land use planning. Originality/value – The paper confirmed the feasibility of the Grey-Markov model in scale prediction of cultivated land.

Allocation of marketing budgets to customer acquisition and retention spending based on decision calculus

2015 ◽  
Vol 10 (2) ◽  
pp. 179-197 ◽  
Author(s):  
Hamidreza Koosha ◽  
Amir Albadvi
Keyword(s):  
Markov Chain Model ◽  
Insurance Industry ◽  
Telecommunication Industry ◽  
Chain Model ◽  
Transition Matrices ◽  
Suggested Approach ◽  
Data Scarcity ◽  
Content Type ◽  
Decision Calculus ◽  
Marketing Budgets

Purpose – The purpose of this paper is to allocate marketing budgets in complex environments, where data are scarce and management judgment is available. In this research, marketing budgets are allocated, to maximize customer equity as a long-term profitability measure. Design/methodology/approach – The researchers provide a model for allocation of marketing budgets based on both decision calculus and econometric models and combine it with the concept of Markov chain model to cope with data scarcity. Dynamic programming is used to find the optimal solution. Findings – The authors examine the model in telecommunication industry. Applicability of the model is supported by an illustrative example. To allocate marketing budgets, researchers consider three strategies for each period: do nothing, retention-focused strategy and acquisition-focused strategy. The results show the applicability and effectiveness of the model to find the best strategy. Research limitations/implications – As the suggested approach is based on management judgment, it is useful in situations, as the authors have experts or experienced managers to achieve reliable data. In situations which the authors do not have access to experienced managers, the results may be unreliable. Practical implications – The suggested approach is useful in situations of data scarcity, where experienced managers are accessible. The researchers have focused on telecommunication industry cases; however, the model is useful in other industries like the insurance industry. Originality/value – The main contribution of the research lies in the suggestion of a new approach to allocate marketing budgets in data scarcity situations in multi-period planning horizons. The researchers use both decision calculus and econometric tools to find the transition matrices of marketing plans.

A grocery recommendation for off-line shoppers

2018 ◽  
Vol 42 (4) ◽  
pp. 468-481 ◽  
Author(s):  
Jae Kyeong Kim ◽  
Hyun Sil Moon ◽  
Byong Ju An ◽  
Il Young Choi
Keyword(s):  
Markov Chain ◽  
Association Rule ◽  
Association Rule Mining ◽  
Markov Chain Model ◽  
Personalized Recommendation ◽  
Chain Model ◽  
Rule Mining ◽  
Content Type ◽  
Sequential Association Rule Mining ◽  
Purchase Patterns

Purpose Many off-line retailers have experienced a slump in sales and have the potential risk of overstock or understock. To overcome these problems, retailers have applied data mining techniques, such as association rule mining or sequential association rule mining, to increase sales and predict product demand. However, because these techniques cannot generate shopper-centric rules, many off-line shoppers are often inconvenienced after writing their shopping lists carefully and comprehensively. Therefore, the purpose of this paper is to propose a personalized recommendation methodology for off-line grocery shoppers. Design/methodology/approach This paper employs a Markov chain model to generate recommendations for the shopper’s next shopping basket. The proposed methodology is based on the knowledge of both purchased products and purchase sequences. This paper compares the proposed methodology with a traditional collaborative filtering (CF)-based system, a bestseller-based system and a Markov-chain-based system as benchmark systems. Findings The proposed methodology achieves improvements of 15.87, 14.06 and 37.74 percent with respect to the CF-, Markov chain-, and best-seller-based benchmark systems, respectively, meaning that not only the purchased products but also the purchase sequences are important elements in the personalization of grocery recommendations. Originality/value Most of the previous studies on this topic have proposed on-line recommendation methodologies. However, because off-line stores collect transaction data from point-of-sale devices, this research proposes a methodology based on purchased products and purchase patterns for off-line grocery recommendations. In practice, this study implies that both purchased products and purchase sequences are viable elements in off-line grocery recommendations.

Degradation modelling and life expectancy using Markov chain model for carriageway

2018 ◽  
Vol 35 (6) ◽  
pp. 1268-1288 ◽  
Author(s):  
Kong Fah Tee ◽  
Ejiroghene Ekpiwhre ◽  
Zhang Yi
Keyword(s):  
Markov Chain ◽  
Life Expectancy ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Transition Probability ◽  
Condition Assessment ◽  
Cumulative Distribution ◽  
Chain Model ◽  
Content Type ◽  
Pairwise Correlation

PurposeAutomated condition surveys have been recently introduced for condition assessment of highway infrastructures worldwide. Accurate predictions of the current state, median life (ML) and future state of highway infrastructures are crucial for developing appropriate inspection and maintenance strategies for newly created as well as existing aging highway infrastructures. The paper aims to discuss these issues.Design/methodology/approachThis paper proposes Markov Chain based deterioration modelling using a linear transition probability (LTP) matrix method and a median life expectancy (MLE) algorithm. The proposed method is applied and evaluated using condition improvement between the two successive inspections from the Surface Condition Assessment of National Network of Roads survey of the UK Pavement Management System.FindingsThe proposed LTP matrix model utilises better insight than the generic or decoupling linear approach used in estimating transition probabilities formulated in the past. The simulated LTP predicted conditions are portrayed in a deterioration profile and a pairwise correlation. The MLs are computed statistically with a cumulative distribution function plot.Originality/valueThe paper concludes that MLE is ideal for projecting half asset life, and the LTP matrix approach presents a feasible approach for new maintenance regime when more certain deterioration data become available.

Setting margins for margin buying in China: balancing the trade-off between liquidity and prudence

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hui Hong ◽  
Chien-Chiang Lee ◽  
Zhicun Bian
Keyword(s):  
Markov Chain ◽  
Stock Prices ◽  
Markov Chain Model ◽  
Stock Exchanges ◽  
Chain Model ◽  
Generation Process ◽  
Trade Off ◽  
Content Type ◽  
Margin Setting ◽  
Buying In

PurposeThe purpose of this paper is to propose a new dynamic margin setting method for margin buying in China and evaluate the validity of its performance with the current margin system adopted by stock exchanges in extreme episodes.Design/methodology/approachThis paper adopts the dynamic conceptual model of Huang et al. (2012) (which is based on Figlewski (1984)) but incorporates Markov chain to describe the data generation process of stock price changes. By applying the model to margin buying contracts for the period of March 16, 2018, to May 2, 2018 (baseline study) and June 15, 2015, to July 27, 2015 (robustness test), the model’s superiority to the current margin system adopted by stock exchanges is also tested.FindingsThe paper has several important findings. First, the margins derived by this system vary with market conditions, rising (declining) when stock prices go down (up), and are generally lower than the requirements imposed by stock exchanges. Second, this margin system induces lower overall percentage of costs than that adopted by stock exchanges. Third, parameter estimation plays an important role on shaping empirical results.Research limitations/implicationsThe primary limitation of this paper lies in the fact that it does not solve the issue of determining optimal parameters of the Markov chain model. On the implication of findings, policy-makers and regulators on supervising margin buying activities may need a tune-up on the current margin system which features static margin requirements. Dynamic margins that incorporate market factors are virtually useful to balance the trade-off between liquidity and prudence.Originality/valueTo the best of the authors’ knowledge, this study is the first of its kind to develop a dynamic margin setting method for margin buying in China, aiming to balance the trade-off between liquidity and prudence. It not only takes into account the uniqueness of Chinese markets but also allows for time variations in both initial and maintenance margins.

A Geologically Based Markov Chain Model for Simulating Tritium Transport With Uncertain Conditions in a Nuclear-Stimulated Natural Gas Reservoir

2009 ◽  
Vol 12 (06) ◽  
pp. 974-984 ◽  
Author(s):  
Ming Ye ◽  
Clay Cooper ◽  
Jenny Chapman ◽  
David Gillespie ◽  
Yong Zhang
Keyword(s):  
Markov Chain ◽  
Numerical Model ◽  
Natural Gas ◽  
Gas Flow ◽  
Markov Chain Model ◽  
Model Development ◽  
Chain Model ◽  
Production Well ◽  
Modeling Framework ◽  
Tritium Transport

Summary Nuclear-stimulation technology, which used subsurface nuclear detonation to increase permeability of tight natural gas reservoirs, was evaluated in the late 1960s and early 1970s. The Rulison site, located in the Piceance basin, Colorado, is one of three sites in the US where the technology was tested. An increase in exploration and production for natural gas in the basin has led to a need to quantify the extent of radionuclide (mainly tritium) migration after the detonation and potential migration under likely production scenarios. To meet this need, a numerical model was developed to simulate gas flow and tritium transport toward a hypothetical production well. A crucial problem in the model development is that limited on-site data are too sparse to quantify uncertainty of subsurface properties. This problem is partly resolved by using indirect data and information, such as parameter measurements from a nearby site and geological information regarding lithofacies geometry. In particular, a geologically based Markov chain model was developed to simulate spatial distribution of the sandstone lithofacies. This paper presents an application of the numerical model for simulating tritium transport from the nuclear chimney toward the production well at a likely location producing at a rate typical for the basin. The results show that under the circumstances considered in this paper, tritium will not reach the production well with a confidence level of 95%. The results also show that the lithofacies structure is more critical in controlling tritium transport than parameters of the sandstone and hydraulically fractured sandstone. The parameters become important only when the connectivity of sandstone lenses exists to support tritium transport from the chimney to the production well. The developed modeling framework can be updated as additional subsurface data are collected. The framework can be used to support establishment of drilling restrictions that protect public health and the environment for different production well scenarios.

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