Impact of decomposition on time series bagging forecasting performance |
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| Institution: | 1. Research Centre for the Competitiveness of the Visitor Economy, School of Hospitality and Tourism Management, University of Surrey, Guildford, GU2 7XH, United Kingdom;2. Hotel and Tourism Research Centre, School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Hong Kong SAR, China;1. School of Tourism and Urban-rural Planning, Zhejiang Gongshang University, Hangzhou, 310018, China;2. School of Management and Marketing, Curtin Business School, Curtin University, WA, 6102, Australia;3. School of Management, Xiamen University, Xiamen, 361005, China;4. Shanghai Development Institute, Shanghai University of Finance and Economics, Shanghai, 200433, China;1. Politecnico di Torino, Department of Management and Production Engineering, And FULL (The Future Urban Legacy Lab), Corso Duca Degli Abruzzi, 24, I-10129, Turin, Italy;2. Modul University Vienna, School of Tourism and Service Management, Am Kahlenberg 1, A-1190, Vienna, Austria;1. Department of Tourism and Hospitality Management, School of Sport, Tourism and Hospitality Management, Temple University, 1810 N. 13th Street, Speakman Hall 332, Philadelphia, PA, 19122, USA;2. School of Hotel Administration, S. C. Johnson College of Business, Cornell University, 448 Statler Hall, Ithaca, NY, 14853, USA;3. College of Business and Management, VinUniversity, Vinhomes Ocean Park, Gia Lam District, Hanoi, Viet Nam;1. College of Tourism and Service Management, Nankai University, Tianjin, 300074, PR China;2. School of Business Administration, Zhongnan University of Economics and Law, 182# Nanhu Avenue, East Lake High-tech Development Zone, Wuhan, 430073, PR China;3. Business School, Hubei University, Wuhan, 430062, PR China;1. School of Economics, University of Nottingham, Ningbo, China;2. Applied Economics Department, Universidad de las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;1. Postdoctoral Research Fellow, Griffith Institute for Tourism, Griffith University, 170 Kessels Rd, Nathan, Brisbane, QLD, 4111, Australia;2. School of Hotel and Tourism Management, The Hong Kong Polytechnic University, 11 Yuk Choi Road, Hung Hom, Hong Kong;3. Department of Tourism, Sport & Hotel Management, Griffith Business School, Griffith University, Nathan Campus, 170 Kessels Rd, Nathan, Brisbane, QLD, 4111, Australia;4. Griffith Institute for Tourism, Griffith University – Gold Coast Campus, 58 Parklands Drive, Southport, Queensland, 4222, Australia;5. Department of Marketing, Griffith Business School, Griffith University – Nathan Campus, 170 Kessels Rd, Nathan, Brisbane, QLD, 4111, Australia |
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| Abstract: | Time series bagging has been deemed an effective way to improve unstable modelling procedures and subsequent forecasting accuracy. However, the literature has paid little attention to decomposition in time series bagging. This study investigates the impacts of various decomposition methods on bagging forecasting performance. Eight popular decomposition approaches are incorporated into the time series bagging procedure to improve unstable modelling procedures, and the resulting bagging methods' forecasting performance is evaluated. Using the world's top 20 inbound destinations as an empirical case, this study generates one-to eight-step-ahead tourism forecasts and compares them against benchmarks, including non-bagged and seasonal naïve models. For short-term forecasts, bagging constructed via seasonal extraction in autoregressive integrated moving average time series decomposition outperforms other methods. An autocorrelation test shows that efficient decomposition reduces variance in bagging forecasts. |
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| Keywords: | Tourism demand Time series forecasting Decomposition Bagging Autocorrelation |
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