|
Adams, Z., & Füss, R., 2010, Macroeconomic determinants of international housing markets. Journal of Housing Economics, 19(1), 38-50. Ahmad, S. N., & Laroche, M., 2017, Analyzing electronic word of mouth: A social commerce construct. International Journal of Information Management, 37(3), 202-213. Akita, R., Yoshihara, A., Matsubara, T., & Uehara, K., 2016, Deep learning for stock prediction using numerical and textual information. In Computer and Information Science (ICIS), 2016 IEEE/ACIS 15th International Conference on (pp. 1-6). IEEE. Anundsen, A. K., Gerdrup, K., Hansen, F., & Kragh‐Sørensen, K., 2016, Bubbles and crises: The role of house prices and credit. Journal of Applied Econometrics, 31(7), 1291-1311. Aoki, K., Proudman, J., & Vlieghe, G., 2004, House prices, consumption, and monetary policy: a financial accelerator approach. Journal of financial intermediation, 13(4), 414-435. Archak, N., Ghose, A., & Ipeirotis, P. G., 2011, Deriving the pricing power of product features by mining consumer reviews. Management science, 57(8), 1485-1509. Ascione, F., Bianco, N., De Stasio, C., Mauro, G. M., & Vanoli, G. P., 2017, CASA, cost-optimal analysis by multi-objective optimization and artificial neural networks: A new framework for the robust assessment of cost-optimal energy retrofit, feasible for any building. Energy and Buildings, 146, 200-219. Azari, M., Tayyebi, A., Helbich, M., & Reveshty, M. A., 2016, Integrating cellular automata, artificial neural network, and fuzzy set theory to simulate threatened orchards: application to Maragheh, Iran. GIScience & Remote Sensing, 53(2), 183-205. Bagliano, F. C., & Morana, C., 2012, The Great Recession: US dynamics and spillovers to the world economy. Journal of Banking & Finance, 36(1), 1-13. Bao, T. Q., & My, B. T. T., 2019, Forecasting stock index based on hybrid artificial neural network models. Science & Technology Development Journal-Economics-Law and Management, 3(1), 52-57. Bayus, B. L., 1985, Word of Mouth-the Indirect Effects of Marketing Efforts. Journal of advertising research, 25(3), 31-39. Beatty, S. E., & Smith, S. M., 1987, External search effort: An investigation across several product categories. Journal of consumer research, 14(1), 83-95. Beracha, E., & Wintoki, M. B., 2013, Forecasting residential real estate price changes from online search activity. Journal of Real Estate Research, 35(3), 283-312. Bernanke, B. S., 2005, The global saving glut and the US current account deficit (No. 77). Bickart, B., & Schindler, R. M., 2001, Internet forums as influential sources of consumer information. Journal of interactive marketing, 15(3), 31-40. Cai, F., 2010, Demographic transition, demographic dividend, and Lewis turning point in China. China Economic Journal, 3(2), 107-119. Can, A., 1992, Specification and estimation of hedonic housing price models. Regional science and urban economics, 22(3), 453-474. Cao, S., Sun, G., Zhang, Z., Chen, L., Feng, Q., Fu, B., ... & Wei, X., 2011, Greening China naturally. AMBIO: A Journal of the Human Environment, 40(7), 828-831. Chan, S., 2001, Spatial lock-in: Do falling house prices constrain residential mobility?. Journal of urban Economics, 49(3), 567-586. Chandar, S. K., Sumathi, M., & Sivanandam, S. N., 2016, Prediction of stock market price using hybrid of wavelet transform and artificial neural network. Indian Journal of Science and Technology, 9(8). Charles, L., Garion, L., & Youngman, L., 2002, Testing alternative theories of the property price-trading volume correlation. Journal of Real Estate Research, 23(3), 253-264. Chen, Y., Gibb, K., Leishman, C., & Wright, R., 2012, The Impact of Population Ageing on House Prices: A Micro‐simulation Approach. Scottish Journal of Political Economy, 59(5), 523-542. Chevalier, J. A., & Mayzlin, D., 2006, The effect of word of mouth on sales: Online book reviews. Journal of marketing research, 43(3), 345-354. Chiou, J. S., & Cheng, C., 2003, Should a company have message boards on its web sites?. Journal of Interactive Marketing, 17(3), 50-61. Choi, H., & Varian, H., 2012, Predicting the present with Google Trends. Economic Record, 88, 2-9. Choi, J. H., Park, H. K., Park, J. E., Lee, C. M., & Choi, B. G., 2018, Artificial Intelligence to forecast new nurse turnover rates in hospital. Journal of the Korea Convergence Society, 9(9), 431-440. Chou, J. S., & Nguyen, T. K., 2018, Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression. IEEE Transactions on Industrial Informatics, 14(7), 3132-3142. Del Negro, M., & Otrok, C., 2007, 99 Luftballons: Monetary policy and the house price boom across US states. Journal of Monetary Economics, 54(7), 1962-1985. Deng, Y., Quigley, J. M., & Van Order, R., 2000, Mortgage terminations, heterogeneity and the exercise of mortgage options. Econometrica, 68(2), 275-307. Dettling, L. J., & Kearney, M. S., 2014, House prices and birth rates: The impact of the real estate market on the decision to have a baby. Journal of Public Economics, 110, 82-100. Din, A., Hoesli, M., & Bender, A., 2001, Environmental variables and real estate prices. Urban studies, 38(11), 1989-2000. Do, A. Q., & Grudnitski, G., 1992, A neural network approach to residential property appraisal. The Real Estate Appraiser, 58(3), 38-45. Ediger, V. Ş., & Akar, S., 2007, ARIMA forecasting of primary energy demand by fuel in Turkey. Energy policy, 35(3), 1701-1708. Engelberg, J., & Gao, P., 2011, In search of attention. The Journal of Finance, 66(5), 1461-1499. Erdogdu, E., 2007, Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey. Energy policy, 35(2), 1129-1146. Fan, G. Z., Ong, S. E., & Koh, H. C., 2006, Determinants of house price: A decision tree approach. Urban Studies, 43(12), 2301-2315. Favilukis, J., Ludvigson, S. C., & Van Nieuwerburgh, S., 2017, The macroeconomic effects of housing wealth, housing finance, and limited risk sharing in general equilibrium. Journal of Political Economy, 125(1), 140-223. Feng, Y., & Jones, K., 2015, Comparing multilevel modelling and artificial neural networks in house price prediction. In Spatial Data Mining and Geographical Knowledge Services (ICSDM), 2015 2nd IEEE International Conference on (pp. 108-114). IEEE. Ferrero, A., 2015, House price booms, current account deficits, and low interest rates. Journal of Money, Credit and Banking, 47(S1), 261-293. Gao, G., Bao, Z., Cao, J., Qin, A. K., Sellis, T., & Wu, Z., 2019, Location-Centered House Price Prediction: A Multi-Task Learning Approach. In arXiv preprint arXiv:1901.01774. Gers, F. A., & Schmidhuber, J., 2000, Recurrent nets that time and count. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium (Vol. 3, pp. 189-194). IEEE. Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L., 2009, Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012. Goldsmith, R. E., & Horowitz, D., 2006, Measuring motivations for online opinion seeking. Journal of interactive advertising, 6(2), 2-14. Graves, A., 2013, Generating sequences with recurrent neural networks. In arXiv preprint arXiv:1308.0850. Green, R., & Hendershott, P. H., 1996, Age, housing demand, and real house prices. Regional Science and Urban Economics, 26(5), 465-480. Greenwood, J., & Hercowitz, Z., 1991, The allocation of capital and time over the business cycle. Journal of political Economy, 99(6), 1188-1214. Han, Q., 2019, Research on Demographic Dividend, Lewis Turning Point, Saving and Economic Growth. International Journal of Business and Social Science, 10(6). Hansen, J., 2009, Australian house prices: a comparison of hedonic and repeat‐sales measures. Economic Record, 85(269), 132-145. Hennig‐Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D., 2004, Electronic word‐of‐mouth via consumer‐opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of interactive marketing, 18(1), 38-52. Hochreiter, S., & Schmidhuber, J., 1997, Long short-term memory. Neural computation, 9(8), 1735-1780. Horrigan, J. B., & Vitak, J., 2008, The Internet and consumer choice: online Americans use different search and purchase strategies for different goods. Pew Internet & American Life Project. Hu, N., Koh, N. S., & Reddy, S. K., 2014, Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decision support systems, 57, 42-53. Huang, B. Q., Cao, G. Y., & Guo, M., 2005, Reinforcement learning neural network to the problem of autonomous mobile robot obstacle avoidance. In 2005 International Conference on Machine Learning and Cybernetics (Vol. 1, pp. 85-89). IEEE. Huynh, H. D., Dang, L. M., & Duong, D., 2017, A New Model for Stock Price Movements Prediction Using Deep Neural Network. In Proceedings of the Eighth International Symposium on Information and Communication Technology (pp. 57-62). ACM. Hwang, M., & Quigley, J. M., 2006, Economic fundamentals in local housing markets: evidence from US metropolitan regions. Journal of regional science, 46(3), 425-453. Ismail, A. R., 2015, Leveraging the potential of word of mouth: The role of love, excitement and image of fashion brands. Journal of Global Fashion Marketing, 6(2), 87-102. Jadevicius, A., & Huston, S., 2015, ARIMA modelling of Lithuanian house price index. International Journal of Housing Markets and Analysis, 8(1), 135-147. Jeon, J. O., & Baeck, S., 2016, The effect of the valence of word-of-mouth on consumers’ attitudes toward co-brands: The moderating roles of brand characteristics. Journal of Global Scholars of Marketing Science, 26(1), 89-108. Jeong, G., & Kim, H. Y., 2019, Improving financial trading decisions using deep q-learning: Predicting the number of shares, action strategies, and transfer learning. Expert Systems with Applications, 117, 125-138. Jud, G. D., & Winkler, D. T., 2002, The dynamics of metropolitan housing prices. The journal of real estate research, 23(1/2), 29-46. Kerstetter, D., & Cho, M. H., 2004, Prior knowledge, credibility and information search. Annals of Tourism Research, 31(4), 961-985. Kim, C. H., & Kim, K. H., 2000, The political economy of Korean government policies on real estate. Urban Studies, 37(7), 1157-1169. Kotler, P., & Keller, K. L., 2003, Marketing Management New Jersey: Prentica Hall. International Eleventh Edition Lassar, Walfried. Krittanawong, C., 2018, The rise of artificial intelligence and the uncertain future for physicians. European journal of internal medicine, 48, e13-e14. Kuruzovich, J., Viswanathan, S., Agarwal, R., Gosain, S., & Weitzman, S., 2008, Marketspace or marketplace? Online information search and channel outcomes in auto retailing. Information Systems Research, 19(2), 182-201. Lacoviello, M., 2005, House prices, borrowing constraints, and monetary policy in the business cycle. American economic review, 95(3), 739-764. Lei, K., Zhang, B., Li, Y., Yang, M., & Shen, Y., 2020, Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading. Expert Systems with Applications, 140, 112872. Leung, C. K. Y., & Feng, D., 2005, What drives the property price-trading volume correlation? Evidence from a commercial real estate market. The Journal of Real Estate Finance and Economics, 31(2), 241-255. Li, J., Bu, H., & Wu, J., 2017, Sentiment-aware stock market prediction: A deep learning method. In Service Systems and Service Management (ICSSSM), 2017 International Conference on (pp. 1-6). IEEE. Li, X., Li, Y., Zhan, Y., & Liu, X. Y., 2019, Optimistic bull or pessimistic bear: adaptive deep reinforcement learning for stock portfolio allocation. arXiv preprint arXiv:1907.01503. Li, Y., Jiang, W., Yang, L., & Wu, T., 2018, On neural networks and learning systems for business computing. Neurocomputing, 275, 1150-1159. Limsombunchai, V., 2004, House price prediction: hedonic price model vs. artificial neural network. In New Zealand Agricultural and Resource Economics Society Conference (25-26). Lin, C. Y., Liaw, S. Y., Chen, C. C., Pai, M. Y., & Chen, Y. M., 2017, A computer-based approach for analyzing consumer demands in electronic word-of-mouth. Electronic Markets, 27(3), 225-242. Litvin, S. W., Goldsmith, R. E., & Pan, B., 2008, Electronic word-of-mouth in hospitality and tourism management. Tourism management, 29(3), 458-468. Ludwig, S., De Ruyter, K., Friedman, M., Brüggen, E. C., Wetzels, M., & Pfann, G., 2013, More than words: The influence of affective content and linguistic style matches in online reviews on conversion rates. Journal of Marketing, 77(1), 87-103. Luo, Q., & Zhong, D., 2015, Using social network analysis to explain communication characteristics of travel-related electronic word-of-mouth on social networking sites. Tourism Management, 46, 274-282. Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F. Y., 2014, Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16(2), 865-873. Malpezzi, S., 1999, A simple error correction model of house prices. Journal of housing economics, 8(1), 27-62. Mankiw, N. G., & Weil, D. N., 1989, The baby boom, the baby bust, and the housing market. Regional science and urban economics, 19(2), 235-258. Marcato, G., & Nanda, A., 2016, Information content and forecasting ability of sentiment indicators: case of real estate market. Journal of Real Estate Research, 38(2), 165-203. Mc Morrow, K., & Roeger, W., 2003, Economic and financial market consequences of ageing populations. Economic papers, (182), 1-96. Mc Morrow, K., & Roeger, W., 2003, Economic and financial market consequences of ageing populations. Economic papers, (182), 1-96. McGreal, S., Adair, A., McBurney, D., & Patterson, D., 1998, Neural networks: the prediction of residential values. Journal of Property Valuation and Investment, 16(1), 57-70. Michelle, P. Y., 2018, Electronic word of mouth influence on consumer purchase intention. Journal of Fundamental and Applied Sciences, 10(3S), 126-141. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M., 2013, Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602. Moe, W. W., & Trusov, M., 2011, The value of social dynamics in online product ratings forums. Journal of Marketing Research, 48(3), 444-456. Mulder, C. H., 2006, Home-ownership and family formation. Journal of Housing and the Built Environment, 21(3), 281-298. Mulder, C. H., & Billari, F. C., 2010, Homeownership regimes and low fertility. Housing Studies, 25(4), 527-541. Nelson, D. M., Pereira, A. C., & de Oliveira, R. A., 2017, Stock market's price movement prediction with LSTM neural networks. In Neural Networks (IJCNN), 2017 International Joint Conference on (pp. 1419-1426). IEEE. Nelson, P., 1970, Information and consumer behavior. Journal of political economy, 78(2), 311-329. Novy‐Marx, R., 2009, Hot and cold markets. Real Estate Economics, 37(1), 1-22. Ortiz, J., Balazinska, M., Gehrke, J., & Keerthi, S. S. (2018). Learning state representations for query optimization with deep reinforcement learning. arXiv preprint arXiv:1803.08604. Otrok, C., & Terrones, M. E., 2005, House prices, interest rates and macroeconomic fluctuations: international evidence. Unpublished manuscript. Özsoy, O., & Şahin, H., 2009, Housing price determinants in Istanbul, Turkey: An application of the classification and regression tree model. International Journal of Housing Markets and Analysis, 2(2), 167-178. Pace, R. K., Barry, R., Gilley, O. W., & Sirmans, C. F., 2000, A method for spatial–temporal forecasting with an application to real estate prices. International Journal of Forecasting, 16(2), 229-246. Park, B., & Bae, J. K., 2015, Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert Systems with Applications, 42(6), 2928-2934. Park, D. H., Lee, J., & Han, I., 2007, The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125-148. Parreco, J., Hidalgo, A., Kozol, R., Namias, N., & Rattan, R., 2018, Predicting mortality in the surgical intensive care unit using artificial intelligence and natural language processing of physician documentation. The American Surgeon, 84(7), 1190-1194. Patel, J., Shah, S., Thakkar, P., & Kotecha, K., 2015, Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268. Rai, B., 2017, Feature selection and predictive modeling of housing data using random forest. International Journal of Industrial and Systems Engineering, 11(4), 5. Rather, A. M., Agarwal, A., & Sastry, V. N., 2015, Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications, 42(6), 3234-3241. Raymond, Y. C., 1997, An application of the ARIMA model to real‐estate prices in Hong Kong. Journal of Property Finance. Regalia, F., & Rios-Rull, J. V., 2001, What accounts for the increase in the number of single households? University of Pennsylvania, mimeo. Ríos-Rull, J. V., 2001, Population changes and capital accumulation: The aging of the baby boom. Advances in Macroeconomics, 1(1). Roondiwala, M., Patel, H., & Varma, S., 2017, Predicting stock prices using LSTM. International Journal of Science and Research, 6(4), 1754-1756. Rosenblatt, F., 1958, The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386. Rumelhart, D. E., & McClelland, J. L, 1982, An interactive activation model of context effects in letter perception: II. The contextual enhancement effect and some tests and extensions of the model. Psychological review, 89(1), 60. Sahba, F., Tizhoosh, H. R., & Salama, M. M., 2006, A reinforcement learning framework for medical image segmentation. In The 2006 IEEE International Joint Conference on Neural Network Proceedings (pp. 511-517). IEEE. Selim, H. (2009). Determinants of house prices in Turkey: Hedonic regression versus artificial neural network. Expert systems with Applications, 36(2), 2843-2852. Silverman, G., 2001, The Power of Word of Mouth, Direct Marketing. Soon, G. K., On, C. K., Rayner, A., Patricia, A., & Teo, J., 2018, A CIMB Stock Price Prediction Case Study with Feedforward Neural Network and Recurrent Neural Network. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(3-2), 89-94. Sutton, R. S., & Barto, A. G., 2018, Reinforcement learning: An introduction. MIT press. Tanimoto, J., & Fujii, H., 2003, A study on diffusional characteristics of information on a human network analyzed by a Multi-Agent simulator. The Social Science Journal, 40(3), 479-485. Taylor, J. B., 2007, Housing and monetary policy (No. w13682). National Bureau of Economic Research. Toth, I. J., & Hajdu, M., 2012, Google as a tool for nowcasting household consumption: estimations on Hungarian data. Proceedings of the 31thConference of Centre for International Research on Economic Tendency Surveys, Vienna, Switzerland. Ullah, S., Javed, N., Hanif, A., and Abdullah, A., 2019, Stock Price Forecast Using Recurrent Neural Network. Data Science, In Proceedings of the International Conference on Data Science, pp. 47-54. Ullman, S., 2019, Using neuroscience to develop artificial intelligence. Science, 363(6428), 692-693. Van Hasselt, H., Guez, A., & Silver, D.,2016, Deep reinforcement learning with double q-learning. In Thirtieth AAAI conference on artificial intelligence. Vargas-Silva, C., 2008, Monetary policy and the US housing market: A VAR analysis imposing sign restrictions. Journal of Macroeconomics, 30(3), 977-990. Varian, H., 2018, Artificial intelligence, economics, and industrial organization (No. w24839). National Bureau of Economic Research. Vosen, S., & Schmidt, T., 2012, A monthly consumption indicator for Germany based on Internet search query data. Applied Economics Letters, 19(7), 683-687. Wallace, N. E., & Meese, R. A, 1997, The construction of residential housing price indices: a comparison of repeat-sales, hedonic-regression, and hybrid approaches. The Journal of Real Estate Finance and Economics, 14(1-2), 51-73. Wang, C. C., & Wang, Y. T., 2010, Persuasion effect of e-WOM: The impact of involvement and ambiguity tolerance. Journal of Global Academy of Marketing, 20(4), 281-293. Wang, X., Wen, J., Zhang, Y., & Wang, Y, 2014, Real estate price forecasting based on SVM optimized by PSO. Optik-International Journal for Light and Electron Optics, 125(3), 1439-1443. Wang, Z., Schaul, T., Hessel, M., Van Hasselt, H., Lanctot, M., & De Freitas, N., 2015, Dueling network architectures for deep reinforcement learning. arXiv preprint arXiv:1511.06581. Watkins, C. J. C. H., 1989, Learning from delayed rewards. Worzala, E., Lenk, M., & Silva, A., 1995, An exploration of neural networks and its application to real estate valuation. Journal of Real Estate Research, 10(2), 185-201. Wu, L., & Brynjolfsson, E., 2009, The future of prediction: how Google searches foreshadow housing prices and quantities. ICIS 2009 Proceedings, 147. WU, X., & ZHOU, X. W., 2016, The Impact of Demographic Dividend on Japanese Economy and Its Enlightenment- An Analysis on the Demographic Changes After the World War II. Journal of Shaanxi Normal University (Philosophy and Social Sciences Edition), (5), 11. Xiong, R., Nichols, E. P., & Shen, Y., 2015, Deep learning stock volatility with google domestic trends. arXiv preprint arXiv:1512.04916. Xiong, Z., Liu, X. Y., Zhong, S., Yang, H., & Walid, A., 2018, Practical deep reinforcement learning approach for stock trading. arXiv preprint arXiv:1811.07522. Xu, X. E., & Chen, T., 2012, The effect of monetary policy on real estate price growth in China. Pacific-Basin Finance Journal, 20(1), 62-77. Yi, J., & Zhang, J., 2010, The effect of house price on fertility: Evidence from Hong Kong. Economic Inquiry, 48(3), 635-650. Yoon, S. J., & Han, H. E., 2012, Experiential approach to the determinants of online word-of-mouth behavior. Journal of Global Scholars of Marketing Science, 22(3), 218-234. Yue, S., & Hongyu, L., 2004, Housing Prices and Economic Fundamentals: A Cross City Analysis of China for 1995—2002 [J]. Economic Research Journal, 6, 78-86. Yunfang, L., & Tiemei, G., 2007, Empirical Analysis on Real Estate Price Fluctuation in Different Provinces of China [J]. Economic Research Journal, 8, 133-142. Zaremba, W., Sutskever, I., & Vinyals, O., 2014, Recurrent neural network regularization. arXiv preprint arXiv:1409.2329. Zhang, G. P, 2003, Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175. Zhang, J., Huang, J., Bo, L. I., & Wei, Y. A. N., 2015, Empirical research on enterprise micro-blogs' word-of-mouth of Sina Weibo. Journal of Tsinghua University (Science and Technology), 54(5), 649-654. 林左裕,2019,應用網路搜尋行為預測房地產市場,應用經濟論叢,(105),219-254。 高慈敏,2014,經濟波動與房地產交易之價量關係:搜索模型之應用,住宅學報,23(2)。 彭建文、蔡怡純,2017,人口結構變遷對房價影響分析,經濟論文叢刊,45(1),163-192。 彭建文、張金鶚,2000,總體經濟對房地產景氣影響之研究,國家科學委員會研究彙刊:人文及社會科學,10。 彭建文,2014,總體經濟與房地產關聯分析,台灣地區2014房地產年鑑,1-21。 鄒欣樺、張金鶚、花敬群,2007,建商不動產表價與議價策略之探討—景氣時機、個案區位及建商類型分析,管理評論,26(3),47-69。 蔡怡純、陳明吉,2004,台北地區住宅市場結構性轉變與價格均衡調整,都市與計劃,31(4),365-390。 彭建文、蔡怡純,2017,人口結構變遷對房價影響分析,經濟論文叢刊,45(1),163-192。 葉怡成,1993,類神經網路模式應用與實作,台北:儒林。
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