預測二手車的價格一直是消費者和汽車經銷商非常感興趣的話題。然而，由於預測二手車價需具備專業知識，且交易價格非常混亂，資訊也不透明，準確預測，並不容易。近年來，由於主要經濟體與開發中國家對汽車的需求暢旺，此外，由於中美貿易戰、新冠肺炎大流行、汽車晶片短缺、通貨膨脹等因素影響，新車供貨不足，導致二手車的需求量顯著提昇。
價格為影響交易量的重要因素，能夠準確預測二手車價，對交易雙方極為重要。唯二手車價受製造年份、里程數、引擎排氣量、車型等諸多因素影響，準確預測車價格，並不容易，且少有研究機構或學者提出有效的解決方案。為解決此問題，本研究以基於優勢之約略集合法 (Dominance Based Rough Set Approach，DRSA) 推衍影響二手車價之核心屬性，作為預測二手車價的自變數，並以二次模糊分段迴歸模型，預測二手車價。
為驗證分析架構之可行性，本研究自知名汽車拍賣網站8891.com.tw下載過去20年Toyota Altis、Honda CRV等六款車型之銷售紀錄3689筆，並以基於優勢之約略集合法擷取車齡、生產區域、品牌等七屬性為核心屬性，其後，以Toyota Altis 之銷售紀錄，預測車價。本究以20年資料中，前15年的資料訓練模型，並以後5年的資料實證模型之準確性。依據實證研究之結果，本研究所提出之模型，可準確預測二手車價。

Predicting the price of second-hand cars has long been a topic of great interest to consumers and car dealers. However, it is not easy to predict the price of a second-hand car accurately because it requires expertise and transaction prices are chaotic and information is not transparent. In recent years, the demand for used cars has increased significantly due to the booming demand for automobiles in major economies and developing countries, as well as the shortage of new cars due to the trade war between China and the United States, the Coronavirus Disease 2019 (COVID-19) pandemic, the shortage of automotive chips, and inflation.
Price is an important factor affecting the transaction volume. It is very important for both sides of the transaction to accurately predict the price of used cars. However, the price of second-hand cars is influenced by numerous factors such as the year of manufacturing, mileage, engine displacement and model. It is not easy to accurately predict the price of cars, and few research institutions or scholars have put forward effective solutions. To resolve this problem, this thesis uses the key attributes of Dominance Based Rough Set Approach (DRSA) as the independent variables for predictions, and then uses a quadratic fuzzy piecewise regression model to predict the prices of second-hand cars.
To verify the feasibility of the analysis framework, this study downloaded 3689 sales records of six car models such as Toyota Altis and Honda CRV in the past 20 years from the famous auto auction website, 8891.com.tw, and extracted seven core attributes, such as car age, production region and brand, by using the DRSA. The sales record of Toyota Altis is used to predict the car price. The first 15 years of data were used to train the model, and the next 5 years were used to verify the accuracy of the model. According to the results of the empirical study, the model proposed in this study can accurately predict the price of used cars.

Abraham, B., & Ledolter, J. (1983). Statistical methods for forecasting (Vol. 179). Hoboken, NJ: Wiley Online Library.
Amron, A. (2018). The influence of brand image, brand trust, product quality, and price on the consumer’s buying decision of MPV cars. European Scientific Journal, 14(13), 228.
Anitescu, C., Atroshchenko, E., Alajlan, N., & Rabczuk, T. (2019). Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials and Continua, 59(1), 345-359.
Askari, S., & Montazerin, N. (2015). A high-order multi-variable fuzzy time series forecasting algorithm based on fuzzy clustering. Expert Systems with Applications, 42(4), 2121-2135.
Bento, A., Roth, K., & Zuo, Y. (2018). Vehicle lifetime trends and scrappage behavior in the US used car market. The Energy Journal, 39(1), 159-183.
Błaszczyński, J., Greco, S., Matarazzo, B., Słowiński, R., & Szela̧g, M. (2013). jMAF-Dominance-based rough set data analysis framework, Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam (pp. 185-209). New York, NY: Springer.
Car Chase. (2020). 9 Factors That Impact The Value of Your Car. Retrieved from https://carchase.com.au/resources/car-valuation-guide/9-factors-that-impact-the-value-of-your-car/
Chatfield, C. (1993). Calculating interval forecasts. Journal of Business & Economic Statistics, 11(2), 121-135.
Chen, & Shyi-Ming. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81(3), 311-319.
Chen, C., Hao, L., & Xu, C. (2017). Comparative analysis of used car price evaluation models. AIP Conference Proceedings, 1839(1), 020165.
Clark, B., Lyons, G., & Chatterjee, K. (2016). Understanding the process that gives rise to household car ownership level changes. Journal of Transport Geography, 55, 110-120.
Cozzi, L., & Petropoulos, A. (2021). Global SUV sales set another record in 2021, setting back efforts to reduce emissions. Retrieved from https://www.iea.org/commentaries/global-suv-sales-set-another-record-in-2021-setting-back-efforts-to-reduce-emissions
Darshan, B. (2018). Influence of social media on vehicle purchasing decisions: An empirical study on automobile industry. International Journal of Mechanical Engineering and Technology, 9(8), 974-981.
Dhanabalan, T., Subha, K., Shanthi, R., & Sathish, A. (2018). Factors influencing consumers’ car purchasing decision in Indian automobile industry. International Journal of Mechanical Engineering and Technology, 9(10), 53-63.
Dooley, G., & Lenihan, H. (2005). An assessment of time series methods in metal price forecasting. Resources Policy, 30(3), 208-217. doi:https://doi.org/10.1016/j.resourpol.2005.08.007
Efendi, R., Ismail, Z., & Deris, M. M. (2013). Improved weight Fuzzy Time Series as used in the exchange rates forecasting of US Dollar to Ringgit Malaysia. International Journal of Computational Intelligence and Applications, 12(01), 1350005.
Elsheikh, A. H., Sharshir, S. W., Abd Elaziz, M., Kabeel, A., Guilan, W., & Haiou, Z. (2019). Modeling of solar energy systems using artificial neural network: A comprehensive review. Solar Energy, 180, 622-639.
Mobility Foresights. (2021). Used Car Market in US Size, Trends, Forecast & Risk 2021-2026. Bengaluru, India: Mobility Foresights.
Gao, K., Sun, L., Yang, Y., Meng, F., & Qu, X. (2021). Cumulative prospect theory coupled with multi-attribute decision making for modeling travel behavior. Transportation Research Part A: Policy and Practice, 148, 1-21.
Gardner Jr, E. S. (1988). A simple method of computing prediction intervals for time series forecasts. Management Science, 34(4), 541-546.
Gegic, E., Isakovic, B., Keco, D., Masetic, Z., & Kevric, J. (2019). Car price prediction using machine learning techniques. TEM Journal, 8(1), 113.
Golder, P. N., Mitra, D., & Moorman, C. (2012). What is quality? An integrative framework of processes and states. Journal of Marketing, 76(4), 1-23.
Greco, S., Matarazzo, B., & Slowinski, R. (2001). Rough sets theory for multicriteria decision analysis. European Journal of Operational Research, 129(1), 1-47.
Greco, S., Matarazzo, B., & Słowiński, R. (2016). Decision rule approach, Multiple criteria decision analysis (pp. 497-552). New York, NY: Springer.
Gu, B., Zhang, T., Meng, H., & Zhang, J. (2021). Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation. Renewable Energy, 164, 687-708.
GUO, R. (2004). Interval-valued fuzzy set modelling of system reliability. In Advanced Reliability Modeling (pp. 157-164): World Scientific.
Hansen, B. E. (2006). Interval forecasts and parameter uncertainty. Journal of Econometrics, 135(1-2), 377-398.
Hoerler, R., Van Dijk, J., Patt, A., & Del Duce, A. (2021). Carsharing experience fostering sustainable car purchasing? Investigating car size and powertrain choice. Transportation Research Part D: Transport and Environment, 96, 102861.
Hojati, M., Bector, C., & Smimou, K. (2005). A simple method for computation of fuzzy linear regression. European Journal of Operational Research, 166(1), 172-184.
Hong, T., Wilson, J., & Xie, J. (2013). Long term probabilistic load forecasting and normalization with hourly information. IEEE Transactions on Smart Grid, 5(1), 456-462.
Huang, Atasu, & Toktay. (2019). Design implications of extended producer responsibility for durable products. Management Science, 65(6), 2573-2590.
Huang, C.-Y., & Tzeng, G.-H. (2008). Multiple generation product life cycle predictions using a novel two-stage fuzzy piecewise regression analysis method. Technological Forecasting and Social Change, 75(1), 12-31.
Huang, C. Y., & Tzeng, G. H. (2008). Multiple generation product life cycle predictions using a novel two-stage fuzzy piecewise regression analysis method. Technological Forecasting and Social Change(1), 12-31.
Huang, G. (2020). When to haggle, when to hold firm? Lessons from the used‐car retail market. Journal of Economics & Management Strategy, 29(3), 579-604.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. Melbourne, Australia: OTexts.
Ismail, Z., & Efendi, R. (2011). Enrollment forecasting based on modified weight fuzzy time series. Journal of Artificial Intelligence, 4(1), 110-118.
Jaafari, A., Zenner, E. K., Panahi, M., & Shahabi, H. (2019). Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agricultural and Forest Meteorology, 266, 198-207.
Jiang, Y., Zhang, Y., Lin, C., Wu, D., & Lin, C.-T. (2020). EEG-based driver drowsiness estimation using an online multi-view and transfer TSK fuzzy system. IEEE Transactions on Intelligent Transportation Systems, 22(3), 1752-1764.
Klempf, Z. (2020). Interview with Cox Automotive Sr. Economist Charlie Chesbrough. Retrieved from https://blog.sellyautomotive.com/blog/interview-with-cox-automotive-sr.-economist-charlie-chesbrough
Koenker, R., & Hallock, K. F. (2001). Quantile regression. Journal of Economic Perspectives, 15(4), 143-156.
Li, G., Wu, D. C., Zhou, M., & Liu, A. (2019). The combination of interval forecasts in tourism. Annals of Tourism Research, 75, 363-378.
Mietzner, D., & Reger, G. (2005). Advantages and disadvantages of scenario approaches for strategic foresight. International Journal Technology Intelligence and Planning, 1(2), 220-239.
Mordor Intelligence. (2020). Used Car Market - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026). Telangana, India: Mordor Intelligence.
Musleh, M. M., Alajrami, E., Khalil, A. J., Abu-Nasser, B. S., Barhoom, A. M., & Naser, S. A. (2019). Predicting Liver Patients using Artificial Neural Network. International Journal of Academic Information Systems Research (IJAISR), 3(10).
Palmer, Z. (2021). Analysts warn of possible used car price plunge in late 2022. Retrieved from https://autos.yahoo.com/analysts-warn-possible-used-car-151000491.html
Pawlak, Z. (1998). Rough set theory and its applications to data analysis. Cybernetics & Systems, 29(7), 661-688.
Pękala, B. (2019). Uncertainty data in interval-valued fuzzy set theory. Properties, Algorithms and Applications, Studies in Fuzziness and Soft Computing, 367.
Prieto, M., Caemmerer, B., & Baltas, G. (2015). Using a hedonic price model to test prospect theory assertions: The asymmetrical and nonlinear effect of reliability on used car prices. Journal of Retailing and Consumer Services, 22, 206-212.
Qian, Y., Liang, J., Song, P., Dang, C., & Wei, W. (2012). Evaluation of the decision performance of the decision rule set from an ordered decision table. Knowledge-based Systems, 36, 39-50.
Rubio, A., Bermúdez, J. D., & Vercher, E. (2017). Improving stock index forecasts by using a new weighted fuzzy-trend time series method. Expert Systems with Applications, 76, 12-20.
Sadaei, H. J., Enayatifar, R., Abdullah, A. H., & Gani, A. (2014). Short-term load forecasting using a hybrid model with a refined exponentially weighted fuzzy time series and an improved harmony search. International Journal of Electrical Power & Energy Systems, 62, 118-129.
Sen, J., & Mehtab, S. (2021). Accurate stock price forecasting using robust and optimized deep learning models. 2021 International Conference on Intelligent Technologies (CONIT), 1-9.
Shen, K.-Y., & Tzeng, G.-H. (2015). A decision rule-based soft computing model for supporting financial performance improvement of the banking industry. Soft Computing, 19(4), 859-874.
Shihabudheen, K., & Pillai, G. N. (2018). Recent advances in neuro-fuzzy system: A survey. Knowledge-Based Systems, 152, 136-162.
Silva, P. C. L., Sadaei, H. J., & Guimarães, F. G. (2016, December). Interval forecasting with Fuzzy Time Series. Paper presented at the 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece.
Song, Q., & Chissom, B. S. (1994). Forecasting enrollments with fuzzy time series—Part II. Fuzzy Sets and Systems, 62(1), 1-8.
Sraders, A., & Lambert, L. (2021). What to expect in the 2022 used car market. Retrieved from https://fortune.com/2021/11/01/used-car-prices-high-carmax-2021/
Steinwart, I., & Christmann, A. (2011). Estimating conditional quantiles with the help of the pinball loss. Bernoulli, 17(1), 211-225.
Talarposhti, F. M., Sadaei, H. J., Enayatifar, R., Guimarães, F. G., Mahmud, M., & Eslami, T. (2016). Stock market forecasting by using a hybrid model of exponential fuzzy time series. International Journal of Approximate Reasoning, 70, 79-98.
Tanaka, H., & Lee, H. (1998). Interval regression analysis by quadratic programming approach. IEEE Transactions on Fuzzy Systems, 6(4), 473-481.
Threewitt, C. (2020). Car Depreciation: What Factors Affect Car Values? Retrieved from https://cars.usnews.com/cars-trucks/car-depreciation-what-factors-affect-car-values
Tseng, Y.-H., Durbin, P., & Tzeng, G.-H. (2001). Using a fuzzy piecewise regression analysis to predict the nonlinear time-series of turbulent flows with automatic change-point detection. Flow, Turbulence and Combustion, 67(2), 81-106.
Tzeng, G.-H., & Shen, K.-Y. (2017). New concepts and trends of hybrid multiple criteria decision making. Boca Raton, Florida: CRC Press.
Ugurlu, U., Oksuz, I., & Tas, O. (2018). Electricity price forecasting using recurrent neural networks. Energies, 11(5), 1255.
Valdes-Dapena, P. (2021). The scorching hot used car market may finally be cooling off. Retrieved from https://edition.cnn.com/2021/07/22/success/used-car-prices-easing-feseries/index.html
Van Rensburg, W. C., & Bambrick, S. (1978). The economics of the world's mineral industries. New York, NY: McGraw-Hill.
Yu, H.-K. (2005). Weighted fuzzy time series models for TAIEX forecasting. Physica A: Statistical Mechanics and Its Applications, 349(3-4), 609-624.
Yu, J.-R., Tzeng, G.-H., & Li, H.-L. (1999). A general piecewise necessity regression analysis based on linear programming. Fuzzy Sets and Systems, 105(3), 429-436.
Yu, J.-R., Tzeng, G.-H., & Li, H.-L. (2001). General fuzzy piecewise regression analysis with automatic change-point detection. Fuzzy Sets and Systems, 119(2), 247-257.
Yu, J.-R., Tzeng, G.-H., & Li, H.-L. (2005). Interval piecewise regression model with automatic change-point detection by quadratic programming. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 13(03), 347-361.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man, and Cybernetics(1), 28-44.
ZeroSum. (2022). What Dealers Can Expect from the Used Car Market in 2022. Retrieved from https://www.zerosum.ai/blog/what-you-can-expect-from-the-used-car-market-2022
Zhou, J., Nekouie, A., Arslan, C. A., Pham, B. T., & Hasanipanah, M. (2020). Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm. Engineering with Computers, 36(2), 703-712.