| Title |
Foreign exchange forecasting models: ARIMA and LSTM comparison |
| Authors |
García, Fernando ; Guijarro, Francisco ; Oliver, Javier ; Tamošiūnienė, Rima |
| DOI |
10.3390/engproc2023039081 |
| Full Text |
|
| Is Part of |
Engineering proceedings: ITISE 2023 : The 9th International Conference on Time Series and Forecasting, Gran Canaria, Spain, 12–14 July 2023.. Basel : MDPI. 2023, vol. 39, iss. 1, art. no. 81, p. 1-7.. eISSN 2673-4591 |
| Keywords [eng] |
ARIMA ; LSTM ; foreign exchange prediction |
| Abstract [eng] |
The prediction of currency prices is important for investors with foreign currency assets, both for speculation and for hedging the exchange rate risk. Classical time series models such as ARIMA models were relevant until the advent of neural networks. In particular, recurrent neural networks such as long short-term memory (LSTM) are show to be a good alternative model for the prediction of short-term stock prices. In this paper, we present a comparison between the ARIMA model and LSTM neural network. A hybrid model that combines the two models is also presented. In addition, the effectiveness of this model on Bitcoin’s future contract is analysed. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2023 |
| CC license |
|
