|
Ayoobi, N., Sharifrazi, D., Alizadehsani, R., Shoeibi, A., Gorriz, J. M., Moosaei, H., Khosravi, A., Nahavandi, S., Chofreh, A. G., & Goni, F. A. (2021). Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results in physics, 27, 104495. Alassafi, M. O., Jarrah, M., & Alotaibi, R. (2022). Time series predicting of COVID-19 based on deep learning. Neurocomputing, 468, 335-344. ASHUTOSH619-SUDO. (2020). M5 Forecasting With Conv1D and LSTM. https://www.kaggle.com/code/ashutosh619sudo/m5-forecasting-with-conv1d-and-lstm Ayoobi, N., Sharifrazi, D., Alizadehsani, R., Shoeibi, A., Gorriz, J. M., Moosaei, H., Khosravi, A., Nahavandi, S., Chofreh, A. G., & Goni, F. A. (2021). Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results in physics, 27, 104495. Bala, R., & Singh, R. P. (2019). Financial and non-stationary time series forecasting using LSTM recurrent neural network for short and long horizon. 2019 10th international conference on computing, communication and networking technologies (ICCCNT), BANERJEE, P. (2020). ARIMA Model for Time Series Forecasting. https://www.kaggle.com/code/prashant111/arima-model-for-time-series-forecasting Chimmula, V. K. R., & Zhang, L. (2020). Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals, 135, 109864. chollet, F. (2022-06). Keras大神歸位:深度學習全面進化!用 Python 實作CNN、RNN、GRU、LSTM、GAN、VAE、Transformer (黃逸華、林采薇, Trans.). 旗標科技股份有限公司. COSMA, A. (2018). Time Series - Exponential Smoothing. https://www.kaggle.com/code/andreicosma/time-series-exponential-smoothing DOBRENZ, D. (2022). Forcasting Using Exponential Smoothing. https://www.kaggle.com/code/danieldobrenz/forcasting-using-exponential-smoothing DUTTA, G. (2020). Temperature Prediction with Exponential Smoothing. https://www.kaggle.com/code/gauravduttakiit/temperature-prediction-with-exponential-smoothing GUPTA, A. (2018). Bidirectional LSTM with Convolution. https://www.kaggle.com/code/eashish/bidirectional-gru-with-convolution Haque, E., Tabassum, S., & Hossain, E. (2021). A comparative analysis of deep neural networks for hourly temperature forecasting. IEEE access, 9, 160646-160660. Jahanbakht, M., Xiang, W., & Azghadi, M. R. (2021). Sea surface temperature forecasting with ensemble of stacked deep neural networks. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. Jin, X., Yu, X., Wang, X., Bai, Y., Su, T., & Kong, J. (2020). Prediction for Time Series with CNN and LSTM. Proceedings of the 11th international conference on modelling, identification and control (ICMIC2019), Kyle. (2021-09-19). [Day4] 時間序列預測界的 OG:白話解釋 ARIMA 組成模型及步驟. https://ithelp.ithome.com.tw/articles/10267730 Li, A. W., & Bastos, G. S. (2020). Stock market forecasting using deep learning and technical analysis: a systematic review. IEEE access, 8, 185232-185242. Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A, 379(2194), 20200209. Lindemann, B., Müller, T., Vietz, H., Jazdi, N., & Weyrich, M. (2021). A survey on long short-term memory networks for time series prediction. Procedia CIRP, 99, 650-655. Livieris, I. E., Pintelas, E., & Pintelas, P. (2020). A CNN–LSTM model for gold price time-series forecasting. Neural computing and applications, 32, 17351-17360. Shastri, S., Singh, K., Kumar, S., Kour, P., & Mansotra, V. (2020). Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study. Chaos, Solitons & Fractals, 140, 110227. Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018). A comparison of ARIMA and LSTM in forecasting time series. 2018 17th IEEE international conference on machine learning and applications (ICMLA), Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2019). The performance of LSTM and BiLSTM in forecasting time series. 2019 IEEE International conference on big data (Big Data), Song, H., Qiu, R. T., & Park, J. (2019). A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting. Annals of Tourism Research, 75, 338-362. Sunny, M. A. I., Maswood, M. M. S., & Alharbi, A. G. (2020). Deep learning-based stock price prediction using LSTM and bi-directional LSTM model. 2020 2nd novel intelligent and leading emerging sciences conference (NILES), Tung, E. R語言自學日記(12) -自迴歸移動平均模型(ARMA模型). https://medium.com/r-%E8%AA%9E%E8%A8%80%E8%87%AA%E5%AD%B8%E7%B3%BB%E5%88%97/r%E8%AA%9E%E8%A8%80%E8%87%AA%E5%AD%B8%E6%97%A5%E8%A8%98-12-%E8%87%AA%E8%BF%B4%E6%AD%B8%E7%A7%BB%E5%8B%95%E5%B9%B3%E5%9D%87%E6%A8%A1%E5%9E%8B-arma%E6%A8%A1%E5%9E%8B-c7bbfa045e05 Wang, Y., Zhu, S., & Li, C. (2019). Research on multistep time series prediction based on LSTM. 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE), Weber. (2021-07-04). ARIMA時間序列模型python應用-銅價格預測(一). https://adaptable-haze-butterfly-551.medium.com/arima%E6%99%82%E9%96%93%E5%BA%8F%E5%88%97%E6%A8%A1%E5%9E%8Bpython%E6%87%89%E7%94%A8-%E9%8A%85%E5%83%B9%E6%A0%BC%E9%A0%90%E6%B8%AC-%E4%B8%80-4f91693e3ec6 Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural computation, 31(7), 1235-1270. Zeroual, A., Harrou, F., Dairi, A., & Sun, Y. (2020). Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study. Chaos, Solitons & Fractals, 140, 110121.
|