scholarly journals Mixed Causal Structure Discovery with Application to Prescriptive Pricing

2018 ◽  
Author(s):  
Wei Wenjuan ◽  
Feng Lu ◽  
Liu Chunchen
Keyword(s):  
Causal Structure ◽  
Demand Forecasting ◽  
Information Criterion ◽  
Causal Learning ◽  
Mixed Data ◽  
Demand Modeling ◽  
Inference Algorithms ◽  
Stage Process ◽  
Price Demand ◽  
Consistent Information

Prescriptive pricing is one of the most advanced pricing techniques, which derives the optimal price strategy to maximize the future profit/revenue by carrying out a two-stage process, demand modeling and price optimization.Demand modeling tries to reveal price-demand laws by discovering causal relationships among demands, prices, and objective factors, which is the foundation of price optimization.Existing methods either use regression or causal learning for uncovering the price-demand relations, but suffer from pain points in either accuracy/efficiency or mixed data type processing, while all of these are actual requirements in practical pricing scenarios.This paper proposes a novel demand modeling technique for practical usage.Speaking concretely, we propose a new locally consistent information criterion named MIC,and derive MIC-based inference algorithms for an accurate recovery of causal structure on mixed factor space.Experiments on simulate/real datasets show the superiority of our new approach in both price-demand law recovery and demand forecasting, as well as show promising performance in supporting optimal pricing.

Modeling Spatiotemporal Heterogeneity of Customer Preferences in Engineering Design

2018 ◽  
Author(s):  
Youyi Bi ◽  
Jian Xie ◽  
Zhenghui Sha ◽  
Mingxian Wang ◽  
Yan Fu ◽  
...  
Keyword(s):  
Engineering Design ◽  
Positive Impact ◽  
Demand Forecasting ◽  
Specific Effect ◽  
Analytical Models ◽  
Strategy Development ◽  
Demand Modeling ◽  
Customer Preferences ◽  
Per Capita ◽  
Spatiotemporal Heterogeneity

Customer preferences are found to evolve over time and correlate with geographical locations. Studying spatiotemporal heterogeneity of customer preferences is crucial to engineering design as it provides a dynamic perspective for a thorough understanding of preference trend. However, existing analytical models for demand modeling do not take the spatiotemporal heterogeneity of customer preferences into consideration. To fill this research gap, a spatial panel modeling approach is developed in this study to investigate the spatiotemporal heterogeneity of customer preferences by introducing engineering attributes explicitly as model inputs in support of demand forecasting in engineering design. In addition, a step-by-step procedure is proposed to aid the implementation of the approach. To demonstrate this approach, a case study is conducted on small SUV in China’s automotive market. Our results show that small SUVs with lower prices, higher power, and lower fuel consumption tend to have a positive impact on their sales in each region. In understanding the spatial patterns of China’s small SUV market, we found that each province has a unique spatial specific effect influencing the small SUV demand, which suggests that even if changing the design attributes of a product to the same extent, the resulting effects on product demand might be different across different regions. In understanding the underlying social-economic factors that drive the regional differences, it is found that Gross Domestic Product (GDP) per capita, length of paved roads per capita and household consumption expenditure have significantly positive influence on small SUV sales. These results demonstrate the potential capability of our approach in handling spatial variations of customers for product design and marketing strategy development. The main contribution of this research is the development of an analytical approach integrating spatiotemporal heterogeneity into demand modeling to support engineering design.

scholarly journals The Development of Causal Reasoning

2017 ◽  
Author(s):  
Paul Muentener ◽  
Elizabeth Bonawitz
Keyword(s):  
Young Children ◽  
Causal Reasoning ◽  
Causal Structure ◽  
Statistical Evidence ◽  
Causal Learning ◽  
Causal Relationships ◽  
Older Children ◽  
Very Young Children ◽  
The World

Research on the development of causal reasoning has broadly focused on accomplishing two goals: understanding the origins of causal reasoning, and examining how causal reasoning changes with development. This chapter reviews evidence and theory that aim to fulfill both of these objectives. In the first section, it focuses on the research that explores the possible precedents for recognizing causal events in the world, reviewing evidence for three distinct mechanisms in early causal reasoning: physical launching events, agents and their actions, and covariation information. The second portion of the chapter examines the question of how older children learn about specific causal relationships. It focuses on the role of patterns of statistical evidence in guiding learning about causal structure, suggesting that even very young children leverage strong inductive biases with patterns of data to inform their inferences about causal events, and discussing ways in which children’s spontaneous play supports causal learning.

scholarly journals Forecasting Tourism Demand with an Improved Mixed Data Sampling Model

2020 ◽  
Vol 60 (2) ◽  
pp. 336-353 ◽  
Author(s):  
Long Wen ◽  
Chang Liu ◽  
Haiyan Song ◽  
Han Liu
Keyword(s):  
Factor Model ◽  
Moving Average ◽  
Demand Forecasting ◽  
Mainland China ◽  
Mixed Data ◽  
Dynamic Factor ◽  
Data Sampling ◽  
Tourism Demand ◽  
Composite Indices ◽  
Mixed Data Sampling

Search query data reflect users’ intentions, preferences and interests. The interest in using such data to forecast tourism demand has increased in recent years. The mixed data sampling (MIDAS) method is often used in such forecasting, but is not effective when moving average (MA) dynamics are involved. To investigate the relevance of the MA components in MIDAS models to tourism demand forecasting, an improved MIDAS model that integrates MIDAS and the seasonal autoregressive integrated moving average process is proposed. Its performance is tested by forecasting monthly tourist arrivals in Hong Kong from mainland China with daily composite indices constructed from a large number of search queries using the generalized dynamic factor model. The forecasting results suggest that this new model significantly outperforms the benchmark model. In addition, comparing the forecasts and nowcasts shows that the latter generally outperforms the former.

scholarly journals Electricity Usage Efficiency and Electricity Demand Modeling in the Case of Germany and the UK

2020 ◽  
Vol 10 (7) ◽  
pp. 2291 ◽  
Author(s):  
Branislav Dudic ◽  
Jan Smolen ◽  
Pavel Kovac ◽  
Borislav Savkovic ◽  
Zdenka Dudic
Keyword(s):  
Demand Forecasting ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Demand Modeling ◽  
Multiple Variable ◽  
Demand Forecasting Model ◽  
Multiple Variable Regression ◽  
The Uk ◽  
Combined Impact

In this article, monthly and yearly electricity consumption predictions for the German power market were calculated using the multiple variable regression model. This model accounts for several factors that are often neglected when forecasting electricity demand in practice, in particular the role of the higher efficiency of electricity usage from year to year. The analysis performed in this paper helps to explain why no growth in power consumption has been observed in Germany during the last decade. It shows that the electricity efficiency usage dataset is a relevant input for the model, which mitigates the combined impact of other factors on the final electricity consumption. The electricity demand forecasting model presented in this article was built in the year 2013 with forecasts for the future years’ electricity demand in Germany provided until 2020. These forecasts and related findings are also evaluated in this article.

scholarly journals Malaysia Tourism Demand Forecasting Using Box-Jenkins Approach

2018 ◽  
Vol 7 (4.30) ◽  
pp. 454 ◽  
Author(s):  
Diyana Izyan Amir Hamzah ◽  
Maria Elena Nor ◽  
Sabariah Saharan ◽  
Noor Fariza Mohd Hamdan ◽  
Nurul Asmaa Izzati Nohamad
Keyword(s):  
Demand Forecasting ◽  
Information Criterion ◽  
Research Paper ◽  
Tourism Industry ◽  
Percentage Error ◽  
Tourism Demand ◽  
Absolute Deviation ◽  
Correct Judgment ◽  
Accurate Forecast ◽  
Future Strategy

Tourism industry in Malaysia is crucial and has contributes a huge part in Malaysia’s economic growth. The capability of forecasting field in tourism industry can assist people who work in tourism-related-business to make a correct judgment and plan future strategy by providing the accurate forecast values of the future tourism demand. Therefore, this research paper was focusing on tourism demand forecasting by applying Box-Jenkins approach on tourists arrival data in Malaysia from 1998 until 2017. This research paper also was aiming to produce the accurate forecast values. In order to achieve that, the error of forecast for each model from Box-Jenkins approach was measured and compared by using Akaike Information Criterion (AIC), Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Model that produced the lowest error was chosen to forecast Malaysia tourism demand data. Several candidate models have been proposed during analysis but the final model selected was SARIMA (1,1,1)(1,1,4)12. It is hoped that this research will be useful in forecasting field and tourism industry.

scholarly journals Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan

BMJ Open ◽  
2017 ◽  
Vol 7 (11) ◽  
pp. e018628 ◽  
Author(s):  
Wang-Chuan Juang ◽  
Sin-Jhih Huang ◽  
Fong-Dee Huang ◽  
Pei-Wen Cheng ◽  
Shue-Ren Wann
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Statistical Tests ◽  
Demand Forecasting ◽  
Information Criterion ◽  
Emergency Department Visits ◽  
Percentage Error ◽  
Initial Development ◽  
Adequate Model ◽  
Ed Visits

ObjectiveEmergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits.MethodsWe retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses.ResultsA series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visitt=7111.161+(at+0.37462 at−1).ConclusionThe ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes.

scholarly journals The Psychology of Causal Perception and Reasoning

2010 ◽  
Author(s):  
David Danks
Keyword(s):  
Causal Reasoning ◽  
Causal Structure ◽  
Psychological Research ◽  
Causal Learning ◽  
Causal Beliefs ◽  
Causal Knowledge ◽  
Causal Relations ◽  
Causal Perception ◽  
The World ◽  
Causal Cognition

Causal beliefs and reasoning are deeply embedded in many parts of our cognition. We are clearly ‘causal cognizers’, as we easily and automatically (try to) learn the causal structure of the world, use causal knowledge to make decisions and predictions, generate explanations using our beliefs about the causal structure of the world, and use causal knowledge in many other ways. Because causal cognition is so ubiquitous, psychological research into it is itself an enormous topic, and literally hundreds of people have devoted entire careers to the study of it. Causal cognition can be divided into two rough categories: causal learning and causal reasoning. The former encompasses the processes by which we learn about causal relations in the world at both the type and token levels; the latter refers to the ways in which we use those causal beliefs to make further inferences, decisions, predictions, and so on.

scholarly journals Least-Square Models with Structural Breaks for Price-Demand Relationship of Prevention-Commodities to Covid-19 Disease: Evidence from Pooled Weekly Data of Hand Sanitizers, Face-Masks and Vitamin-C Supplements

2020 ◽  
Vol V (III) ◽  
pp. 166-182
Author(s):  
Aziz Ahmed ◽  
Nagina Gul ◽  
Muhammad Shakeel
Keyword(s):  
Vitamin C ◽  
Structural Breaks ◽  
A Priori ◽  
Sampling Technique ◽  
Information Criterion ◽  
Graphical Analysis ◽  
Least Square ◽  
World Health ◽  
Price Demand ◽  
Relationship Of

The present study employs log-log models and comparatively estimates the price-demand relationship of hand sanitizers, face-masks and vitamin-c supplements as COVID-19 prevention-commodities both before and after the exponential spread of the current pandemic. A novel weekly data is collected and generated by a two-stage stratified sampling technique in a field survey from five capital and most populous cities of Pakistan. The least-square methods with structural breaks are employed for city-wise assessment, while fully modified ordinary least square methods are employed for the pooled panel of Pakistan. The structural breaks are assessed using a priori information via graphical analysis as well as employing the global information criterion of structural breaks. The findings suggest demands and prices structurally surged exponentially after the spread of COVID-19 disease in Pakistan. Thus, implying; i) immediate policy check to stabilize the prices of COVID- 19 prevention-commodities for the poor and lower-income groups of people and ii) importing the commodities to fill the existing demand-supply gap to mitigate the spread of the current pandemic as per the World Health Organization benchmark for public prepared-ness to combat COVID-19 disease.

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