The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
A Comparison between ARIMA, LSTM, and GRU for Time Series Forecasting
Abstract
A critical area of machine learning is Time Series forecasting, as various forecasting problems contain a time component. A series of observations taken chronologically in time is known as a Time Series. In this research, however, we aim to compare three different machine learning models in making a time series forecast. We are going to use the Bitcoin's price dataset as our time series data set and make predictions accordingly. The results show that the ARIMA model gave better results than the deep learning-based regression models. ARIMA gives the best results at 2.76% and 302.53 for MAPE and RMSE respectively. The Gated Recurrent Unit (GRU) however performed better than the Long Short-term Memory (LSTM), with 3.97% and 381.34 of MAPE and RMSE respectively.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Bitcoin: A Peer-to-Peer Electronic Cash System
Wright, C. S. (2008) · SSRN Electronic Journal · doi:10.2139/ssrn.3440802 · Cited by 13,922
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-24271-9 · Cited by 2,370
Géron, A. (2017) · Cited by 1,443
Predicting the Price of Bitcoin Using Machine Learning
McNally, S., Roche, J., & Caton, S. (2018) · 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) · doi:10.1109/pdp2018.2018.00060 · Cited by 665
EA-LSTM: Evolutionary attention-based LSTM for time series prediction
Li, Y., Zhu, Z., Kong, D., Han, H., & Zhao, Y. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.05.028 · Cited by 489
2017 Global Cryptocurrency Benchmarking Study
Hileman, G., & Rauchs, M. (2017) · doi:10.2139/ssrn.2965436 · Cited by 466
Online ARIMA Algorithms for Time Series Prediction
Liu, C., Hoi, S. C. H., Zhao, P., & Sun, J. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10257 · Cited by 191
Global Cryptocurrency Benchmarking StudyPreprint
Rauchs, M., & Hileman, G. (2017) · RePEc: Research Papers in Economics · Cited by 101
Cited by
Data-driven learning-based Model Predictive Control for energy-intensive systems
Chen, J., Xu, G., & Zhou, Z. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102208 · Cited by 34
Metadata
- Authors
- Yamak, P. T., Yujian, L., & Gadosey, P. K.
- Venue
- Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence
- Publisher
- ACM
- Published
- 2019-12-20
- Pages
- 49-55
- OpenAlex
- W3005448318
- Cited by
- 380
- References
- 9
- FWCI
- 31.58
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yamak, P. T., Yujian, L., & Gadosey, P. K. (2019). A Comparison between ARIMA, LSTM, and GRU for Time Series Forecasting. Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence. https://doi.org/10.1145/3377713.3377722
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