Pemodelan Time Series untuk Peramalan Web Traffic Menggunakan Algoritma Arima, LSTM, dan GRU
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Feb 12, 2022
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Abstract
Saat ini peramalan lalu lintas web merupakan masalah besar karena hal ini dapat menyebabkan kemunduran pada cara kerja utama situs web. Peramalan deret waktu telah menjadi topik hangat untuk penelitian. Meramalkan Web Traffic dataset dari Kaggle dan mengevaluasi keakuratan melalui hasil peramalan akan menjadi salah satu cara yang efisien untuk menyampaikan informasi. Kami mengusulkan menggunakan metode tradisional dan metode baru yaitu Autoregressive Integrated Moving Average (ARIMA), Long-Short Term Memory (LSTM), dan Gated Recurrent Unit (GRU). Penelitian ini bertujuan untuk membandingkan pemodelan time series pada peramalan web traffic dengan algoritma Arima, LSTM, dan GRU.
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Arissinta, I., Sulistiyawati, I., Kurnianto, D., & Kharisudin, I. (2022). Pemodelan Time Series untuk Peramalan Web Traffic Menggunakan Algoritma Arima, LSTM, dan GRU. PRISMA, Prosiding Seminar Nasional Matematika, 5, 693-700. Retrieved from https://journal.unnes.ac.id/sju/prisma/article/view/54712
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References
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Casado-Vara, R., del Rey, A. M., Pérez-Palau, D., De-La-fuente-valentín, L., & Corchado, J. M. (2021). Article web traffic time series forecasting using LSTM neural networks with distributed asynchronous training. Mathematics, 9(4), 1–22. https://doi.org/10.3390/math9040421
Chen, W. C., Chen, W. H., & Yang, S. Y. (2018). A big data and time series analysis technology-based multi-agent system for smart tourism. Applied Sciences (Switzerland), 8(6). https://doi.org/10.3390/app8060947
Le, P., & Zuidema, W. (2014). Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs. 2010.
Madan, R. (2018). Predicting Computer Network Traffic : A Time Series Forecasting Approach using DWT , ARIMA and RNN. 1, 2–4.
Mehrmolaei, S. (n.d.). Time series forecasting using improved ARIMA. 92–97.
Paun, K. P., & Makwana, C. H. (2019). A Survey on Web Traffic Forecasting on Time Series Data. VI(3588), 3588–3594.
Petluri, N., & Al-Masri, E. (2019). Web Traffic Prediction of Wikipedia Pages. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 5427–5429. https://doi.org/10.1109/BigData.2018.8622207
Shelatkar, T., Tondale, S., Yadav, S., & Ahir, S. (2020). Web Traffic Time Series Forecasting using ARIMA and LSTM RNN. ITM Web of Conferences, 32, 03017. https://doi.org/10.1051/itmconf/20203203017
Shen, G., Tan, Q., Zhang, H., Zeng, P., & Xu, J. (2018). ScienceDirect ScienceDirect Deep Learning with Gated Recurrent Unit Networks for Financial Deep Learning with Gated Recurrent Unit Networks for Financial Sequence Predictions Sequence Predictions. Procedia Computer Science, 131, 895–903. https://doi.org/10.1016/j.procs.2018.04.298
Zhao, H., Chen, Z., Jiang, H., Jing, W., & Sun, L. (2019). Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series — A Case Study in Zhanjiang , China. November. https://doi.org/10.3390/rs11222673