| Reference: | 一、中文文獻
1.柯朝斌（2011）。台灣啤酒市場概況暨進口啤酒選擇因素之研究. 餐旅暨家政學刊, 8(1), 1-19.
2.紀世訓（1995）。時間數列模式與神經網路在長壽捲菸銷量預測之應用. 空大行政學報 4, 1995, 335-368。
3.陳宇勛（2020）。建構季節性產品之銷售預測模式：以18天生啤酒為例。國立交通大學管理學院運輸物流學程碩士論文。
4.廖子儀（2018）。原物料商品價格之預測-以ARIMA模型分析。國立高雄應用科技大學國際企業研究所碩士論文。
二、外文文獻
1.Ahnaf, M. S., Kurniawati, A., & Anggana, H. D. (2021, September). Fore-casting pet food item stock using arima and lstm. In 2021 4th Interna-tional Conference of Computer and Informatics Engineering (IC2IE) (pp. 141-146). IEEE.
2.Bandara, K., Shi, P., Bergmeir, C., Hewamalage, H., Tran, Q., & Seaman, B. (2019). Sales demand forecast in e-commerce using a long short-term memory neural network methodology. In Neural Information Processing: 26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12–15, 2019, Proceedings, Part III 26 (pp. 462-474). Springer International Publishing.
3.Bratina, D., & Faganel, A. (2008). Forecasting the primary demand for a beer brand using time series analysis. Organizacija, 41(3).
4.Dai, Y., & Huang, J. (2021, February). A sales prediction method based on lstm with hyper-parameter search. In Journal of Physics: Conference Se-ries (Vol. 1756, No. 1, p. 012015). IOP Publishing.
5.Dave, E., Leonardo, A., Jeanice, M., & Hanafiah, N. (2021). Forecasting Indonesia exports using a hybrid model ARIMA-LSTM. Procedia Com-puter Science, 179, 480-487.
6.Elmasdotter, A., & Nyströmer, C. (2018). A comparative study between LSTM and ARIMA for sales forecasting in retail.
7.Evans, S. (2020). The Effects of Weather Variance on Local Beer Sales.
8.Fredén, D., & Larsson, H. (2020). Forecasting daily supermarkets sales with machine learning.
9.Hong, J. K. (2021). LSTM-based Sales Forecasting Model. KSII Transac-tions on Internet & Information Systems, 15(4).
10.Kamdem, J. S., Essomba, R. B., & Berinyuy, J. N. (2020). Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities. Chaos, Solitons & Fractals, 140, 110215.
11.Lewis, C. D. (1982). Industrial and business forecasting methods: A prac-tical guide to exponential smoothing and curve fitting.
12.Mehtab, S., & Sen, J. (2020). A time series analysis-based stock price prediction using machine learning and deep learning models. Internation-al Journal of Business Forecasting and Marketing Intelligence, 6(4), 272-335.
13.Nikolopoulos, K., & Fildes, R. (2013). Adjusting supply chain forecasts for short-term temperature estimates: a case study in a Brewing company. IMA Journal of Management Mathematics, 24(1), 79-88.
14.Olah, C. (2015). Understanding lstm networks.
15.Palkar, A., Deshpande, M., Kalekar, S., & Jaswal, S. (2020, July). Demand Forecasting in Retail Industry for Liquor Consumption using LSTM. In 2020 International Conference on Electronics and Sustainable Communi-cation Systems (ICESC) (pp. 521-525). IEEE.
16.Ramachandran, K. K. Prediction supermarket sales with big data analyt-ics : A comparative study if machine learning techniques. Journal ID, 6202, 8020.
17.Shen, J., & Shafiq, M. O. (2020). Short-term stock market price trend prediction using a comprehensive deep learning system. Journal of big Data, 7(1), 1-33.
18.Siami-Namini, S., & Namin, A. S. (2018). Forecasting economics and fi-nancial time series: ARIMA vs. LSTM. arXiv preprint arXiv:1803.06386.
19.Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2019). A comparative analysis of forecasting financial time series using arima, lstm, and bilstm. arXiv preprint arXiv:1911.09512.
20.Vavliakis, K. N., Siailis, A., & Symeonidis, A. L. (2021). Optimizing Sales Forecasting in e-Commerce with ARIMA and LSTM Models. In WEBIST (pp. 299-306). |
