LSTM Hyperparameters optimization with Hparam parameters for Bitcoin Price Prediction

I.sibel Kervancı; Fatih Akay

doi:10.35377/saucis...1172027

EN

LSTM Hyperparameters optimization with Hparam parameters for Bitcoin Price Prediction

Abstract

Machine learning and deep learning algorithms produce very different results with different examples of their hyperparameters. Algorithm parameters require optimization because they aren't specific for all problems. In this paper Long Short-Term Memory (LSTM), eight different hyperparameters (go-backward, epoch, batch size, dropout, activation function, optimizer, learning rate and, number of layers) were used to examine to daily and hourly Bitcoin datasets. The effects of each parameter on the daily dataset on the results were evaluated and explained These parameters were examined with hparam properties of Tensorboard. As a result, it was seen that examining all combinations of parameters with hparam produced the best test Mean Square Error (MSE) values with hourly dataset 0.000043633 and daily dataset 0.00073843. Both datasets produced better results with the tanh activation function. Finally, when the results are interpreted, the daily dataset produces better results with a small learning rate and small dropout values, whereas the hourly dataset produces better results with a large learning rate and large dropout values.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence

Journal Section

Research Article

Authors

I.sibel Kervancı ^*
0000-0001-5547-1860
Türkiye

Fatih Akay
0000-0003-0780-0679
Türkiye

Early Pub Date

April 28, 2023

Publication Date

April 30, 2023

Submission Date

September 7, 2022

Acceptance Date

January 2, 2023

Published in Issue

Year 2023 Volume: 6 Number: 1

DOI

https://doi.org/10.35377/saucis...1172027

IZ

https://izlik.org/JA57DY85DR

Cite

RIS / Bibtex

APA

Kervancı, I., & Akay, F. (2023). LSTM Hyperparameters optimization with Hparam parameters for Bitcoin Price Prediction. Sakarya University Journal of Computer and Information Sciences, 6(1), 1-9. https://doi.org/10.35377/saucis...1172027

AMA

1.Kervancı I, Akay F. LSTM Hyperparameters optimization with Hparam parameters for Bitcoin Price Prediction. SAUCIS. 2023;6(1):1-9. doi:10.35377/saucis.1172027

Chicago

Kervancı, I.sibel, and Fatih Akay. 2023. “LSTM Hyperparameters Optimization With Hparam Parameters for Bitcoin Price Prediction”. Sakarya University Journal of Computer and Information Sciences 6 (1): 1-9. https://doi.org/10.35377/saucis. 1172027.

EndNote

Kervancı I, Akay F (April 1, 2023) LSTM Hyperparameters optimization with Hparam parameters for Bitcoin Price Prediction. Sakarya University Journal of Computer and Information Sciences 6 1 1–9.

IEEE

[1]I. Kervancı and F. Akay, “LSTM Hyperparameters optimization with Hparam parameters for Bitcoin Price Prediction”, SAUCIS, vol. 6, no. 1, pp. 1–9, Apr. 2023, doi: 10.35377/saucis...1172027.

ISNAD

Kervancı, I.sibel - Akay, Fatih. “LSTM Hyperparameters Optimization With Hparam Parameters for Bitcoin Price Prediction”. Sakarya University Journal of Computer and Information Sciences 6/1 (April 1, 2023): 1-9. https://doi.org/10.35377/saucis. 1172027.

JAMA

1.Kervancı I, Akay F. LSTM Hyperparameters optimization with Hparam parameters for Bitcoin Price Prediction. SAUCIS. 2023;6:1–9.

MLA

Kervancı, I.sibel, and Fatih Akay. “LSTM Hyperparameters Optimization With Hparam Parameters for Bitcoin Price Prediction”. Sakarya University Journal of Computer and Information Sciences, vol. 6, no. 1, Apr. 2023, pp. 1-9, doi:10.35377/saucis. 1172027.

Vancouver

1.I.sibel Kervancı, Fatih Akay. LSTM Hyperparameters optimization with Hparam parameters for Bitcoin Price Prediction. SAUCIS. 2023 Apr. 1;6(1):1-9. doi:10.35377/saucis. 1172027

LSTM Hyperparameters optimization with Hparam parameters for Bitcoin Price Prediction

Öz

LSTM Hyperparameters optimization with Hparam parameters for Bitcoin Price Prediction

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

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