Impact of Chaining Method and Level of Completion on Accuracy of Function Structure-Based Market Price Prediction Models

Volume 1: 37th Computers and Information in Engineering Conference ◽

10.1115/detc2017-68451 ◽

2017 ◽

Author(s):

Amaninder Singh Gill ◽

Joshua D. Summers

Keyword(s):

Prediction Models ◽

Market Price ◽

Function Structure ◽

Topological Information ◽

Market Prices ◽

Forward Chaining ◽

Price Prediction ◽

Accuracy And Precision ◽

Model Completion ◽

Different Levels

The goal of this paper is to explore how different modeling approaches to construct function structure models and different levels of model completion affect the information contained within the respective models. Specifically, the models are used to predict market prices of products. These predictions are compared based on their accuracy and precision. This work is based on previous studies on understanding how function modeling is done and how topological information from design graphs can be used to predict information with historical training. It was found that forward chaining was the least favorable chaining type irrespective of the level of completion. Backward chaining models work relatively better across all completion percentages, while Nucleation models don’t perform as well for a higher completion percentage. Hence, a greater attention is needed to understand and employ the methods yielding the most accuracy.

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Impact of Chaining Method and Level of Completion on Accuracy of Function Structure-Based Market Price Prediction Models

Journal of Computing and Information Science in Engineering ◽

10.1115/1.4043210 ◽

2019 ◽

Vol 19 (4) ◽

Author(s):

Amaninder Singh Gill ◽

Joshua D. Summers ◽

Chiradeep Sen

Keyword(s):

Prediction Models ◽

Market Price ◽

Poor Performance ◽

Future Research ◽

Function Structure ◽

Market Prices ◽

Accuracy And Precision ◽

Simulation Based ◽

Function Models ◽

Model Completion

AbstractThe goal of this paper is to explore how different modeling approaches for constructing function structure models and different levels of model completion affect the ability to make inferences (reason) on the resulting information within the respective models. Specifically, the function structure models are used to predict market prices of products, predictions that are then compared based on their accuracy and precision. This work is based on previous studies on understanding how function modeling and the use of topological information from design graphs can be used to predict information with historical training. It was found that forward chaining was the least favorable chaining type irrespective of the level of completion, whereas the backward-chaining models performed relatively better across all completion levels. Given the poor performance of the nucleation models at the highest level of completion, future research must be directed toward understanding and employing the methods yielding the most accuracy. Moreover, the results from this simulation-based study can be used to develop modeling guidelines for designers or students, when constructing function models.

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Impact of Level of Detail and Information Content on Accuracy of Function Structure-Based Market Price Prediction Models

Volume 1B: 36th Computers and Information in Engineering Conference ◽

10.1115/detc2016-59662 ◽

2016 ◽

Cited By ~ 5

Author(s):

Amaninder Singh Gill ◽

Joshua D. Summers ◽

Cameron J. Turner

Keyword(s):

Prediction Models ◽

Market Price ◽

Level Of Detail ◽

Research Approach ◽

Function Structure ◽

Amount Of Information ◽

Market Values ◽

Grammar Rules ◽

Model Set ◽

Function Structures

This paper explores the amount of information stored in the representational components of a function structure: vocabulary, grammar, and topology. This is done by classifying the previously developed functional composition rules into vocabulary, grammatical, and topological classes and applying them to function structures available in an external design repository. The pruned function structures of electromechanical devices are then evaluated for how accurately market values can be predicted using graph complexity connectivity method. The accuracy is inversely with amount of information and level of detail. Applying the topological rule does not significantly impact the predictive power of the models, while applying the vocabulary rules and the grammar rules reduce the accuracy of the predictions. Finally, the least predictive model set is that which had all rules applied. In this manner, the value of a representation to predict or answer questions is quantified through this research approach.

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Improving the Electricity Price Prediction Accuracy by Applying Combined Prediction Models

Vestnik MEI ◽

10.24160/1993-6982-2020-6-119-128 ◽

2020 ◽

Vol 6 (6) ◽

pp. 119-128

Author(s):

Anna V. Shikhina ◽

◽

Tatyana V. Yagodkina ◽

Keyword(s):

Electricity Markets ◽

Prediction Accuracy ◽

Regression Models ◽

Prediction Models ◽

Free Market ◽

Moving Average ◽

Market Price ◽

Electricity Price ◽

Multifactor Models ◽

Price Prediction

The solution of problems concerned with predicting a free market price for electricity through constructing different prediction models is considered. In so doing, a shift is made from an analysis of conventional regression and auto-regression models of the moving average to the proposed combined multifactor models, which also include the time trend and dummy variables. This shift is partly justified by the specific behavior of the electricity price in the free market, which is caused by a strictly cyclic change of its value, e.g., proceeding from such attributes as the heating season, day of week, etc. The techniques of constructing combined prediction models has been developed to the level of elaborating effective computational procedures based on the Statistica and OsiSoft PI-System software packages. The application of the autoregressive and combined regression prediction models to the Russian market has demonstrated their fairly good effectiveness with an acceptable level of accuracy. A comparison of the achieved levels of accuracy provided by the competing models has not shown any advantages of the shift to the use of combined regression multifactor models in terms of achieving better prediction accuracy; however, their application for analyzing the influence of different factors on the predicted variable may become a fundamental advantage in selecting the type of prediction model. Despite their being limited to an analysis of the Belgorod region market, the obtained results demonstrate the achieved prediction accuracy that is as least as good, and in the main is even better than the majority of the data presented in the review of the results for European electricity markets. The article substantiates the advisability of studying the combined regression models as a tool for analyzing the influence of individual factors as components influencing the electricity price formation for the predicted period, given that the accuracy level of the combined regression models corresponds to the currently achieved electricity price prediction accuracy levels.

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A Historical Data based Ensemble System for Efficient Stock Price Prediction

Recent Patents on Computer Science ◽

10.2174/2213275912666190730161807 ◽

2019 ◽

Vol 12 ◽

Author(s):

Vijay Kumar Dwivedi ◽

Manoj Madhava Gore

Keyword(s):

Stock Price ◽

Prediction Models ◽

Historical Data ◽

Stock Exchange ◽

Primary Objective ◽

Lasso Regression ◽

Stock Price Prediction ◽

Price Prediction ◽

Consistent Manner ◽

Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

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Study of Price Determinants of Sharing Economy-Based Accommodation Services: Evidence from Airbnb.com

Journal of theoretical and applied electronic commerce research ◽

10.3390/jtaer16040035 ◽

2020 ◽

Vol 16 (4) ◽

pp. 584-601

Author(s):

Chunwei Chang ◽

Shengli Li

Keyword(s):

Prediction Models ◽

Interaction Effects ◽

Sharing Economy ◽

Tourist Attractions ◽

Price Prediction ◽

Price Determinants

This research aims to identify price determinants for sharing economy-based accommodation services and to further use the identified price determinants to predict accommodation prices. A dataset drawn from Airbnb.com, was collected for analysis. We identify price determinants from five categories. The top five price determinants are identified as room type, city, distance to tourist attractions, number of pictures posted, and number of amenities provided. More importantly, we find that interaction effects between variables can also significantly influence price. Finally, a series of price prediction models are built based on the identified price determinants.

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Short-Term Stock Price Prediction Models Based on Economic Background

Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering ◽

10.1145/3452940.3452947 ◽

2020 ◽

Author(s):

Ruien Luo

Keyword(s):

Stock Price ◽

Prediction Models ◽

Short Term ◽

Stock Price Prediction ◽

Price Prediction ◽

Economic Background

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UJI TINGKAT KESESUAIAN NILAI JUAL OBJEK PAJAK DENGAN MENGGUNAKAN APLIKASI ASSESSMENT RATIO

INFO ARTHA ◽

10.31092/jia.v5i2.1207 ◽

2021 ◽

Vol 5 (1) ◽

pp. 45-53

Author(s):

Swasito Adhipradana Prabu

Keyword(s):

Market Price ◽

Current Level ◽

Accuracy Rate ◽

Tax Base ◽

Market Prices ◽

Administration System ◽

Positive Correlation ◽

Big Cities ◽

Better Than

The decentralization of PBB-P2 in Indonesia is expected to produce a better PBB-P2 administration system. One indicator of a better PBB-P2 administration system is a fair collection of PBB-P2 based on tax base (NJOP) valuation close to market prices. This study examines whether NJOP, as the basis for the imposition of PBB-P2, is in accordance with the market price using the assessment ratio. This study found that the current level of accuracy of the NJOP has not met the standard agreed upon by the IAAO. In addition, this study also found that the NJOP accuracy rate in big cities was slightly better than the NJOP accuracy rate in other cities. In addition, this study also found that there was no positive correlation between NJOP updating activities through SPOP filling and NJOP accuracy. Desentralisasi PBB-P2 di Indonesia diharapkan menghasilkan sistem penatausahaan PBB-P2 yang lebih baik. Salah satu indikator dari sistem penatausahaan PBB-P2 yang lebih baik adalah pemungutan PBB-P2 yang adil dengan dasar pengenaan pajak (NJOP) yang mendekati harga pasar. Studi ini meneliti apakah NJOP sebagai dasar pengenaan PBB-P2 sudah sesuai dengan harga pasar menggunakan assessment ratio. Penelitian ini menemukan bahwa tingkat akurasi NJOP saat ini belum memenuhi standar yang disepakati oleh IAAO. Selain itu, penelitian ini juga menemukan bahwa tingkat akurasi NJOP di kota besar, sedikit lebih baik dibanding tingkaat akurasi NJOP di kota-kota lainnya. Selain itu, penelitian ini juga menemukan bahwa tidak ada korelasi positif antara kegiatan pemutakhiran NJOP melalui pengisian SPOP dengan tingkat akurasi NJOP.

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Fractional differencing in stock market price and online presence of global tourist corporations

Journal of Economics Finance and Administrative Science ◽

10.1108/jefas-01-2018-0013 ◽

2019 ◽

Vol 24 (48) ◽

pp. 194-204 ◽

Cited By ~ 1

Author(s):

Francisco Flores-Muñoz ◽

Alberto Javier Báez-García ◽

Josué Gutiérrez-Barroso

Keyword(s):

Time Series ◽

Stock Market ◽

Market Price ◽

Google Trends ◽

Data Sets ◽

Market Prices ◽

Content Type ◽

Search Results ◽

Online Presence ◽

Fractional Differencing

Purpose This work aims to explore the behavior of stock market prices according to the autoregressive fractional differencing integrated moving average model. This behavior will be compared with a measure of online presence, search engine results as measured by Google Trends. Design/methodology/approach The study sample is comprised by the companies listed at the STOXX® Global 3000 Travel and Leisure. Google Finance and Yahoo Finance, along with Google Trends, were used, respectively, to obtain the data of stock prices and search results, for a period of five years (October 2012 to October 2017). To guarantee certain comparability between the two data sets, weekly observations were collected, with a total figure of 118 firms, two time series each (price and search results), around 61,000 observations. Findings Relationships between the two data sets are explored, with theoretical implications for the fields of economics, finance and management. Tourist corporations were analyzed owing to their growing economic impact. The estimations are initially consistent with long memory; so, they suggest that both stock market prices and online search trends deserve further exploration for modeling and forecasting. Significant differences owing to country and sector effects are also shown. Originality/value This research contributes in two different ways: it demonstrate the potential of a new tool for the analysis of relevant time series to monitor the behavior of firms and markets, and it suggests several theoretical pathways for further research in the specific topics of asymmetry of information and corporate transparency, proposing pertinent bridges between the two fields.

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