Vanilla Recurrent Neural Network (RNN) have feedback loop that do not throw away the past information in the learning process. Vanishing gradient often happen in the learning process of Vanilla RNN effect failed keep long term dependencies and decrease accuration. Long Short-Term Memory (LSTM) solved its problem. The two methods used for forecasting Moderna Inc stock price which is currently producing a covid vaccine. The daily closed stock price used here were collected over the december 10th, 2018 until March 31th, 2022 which divided 80% training data and 20% testing data. Comparison between two methods using Mean Absolute Error (MAE) in testing data. Input layer, based on difference and partial autocorrelation functions, are lag 1, 3, 4, 5 and 6. The best method is LSTM with one hidden layer consist of 50 units and 91 epochs, yielded MEA 0.0076. Forecasting the next 167 days starting from April 1st, 2022, has an uptrend until 239.85.
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4 October 2023
THE 10TH INTERNATIONAL BASIC SCIENCE INTERNATIONAL CONFERENCE (BASIC) 2022
13–14 September 2022
Malang, Indonesia
Research Article|
October 04 2023
Comparing recurrent neural network and long short term memory for forecasting stock price of Moderna Inc
Dwi Ayu Lusia;
Dwi Ayu Lusia
a)
1
Department Statistics, Faculty of Mathematics and Sciences, Universitas Brawijaya
, Malang, Indonesia
, 65145a)Corresponding author: [email protected]
Search for other works by this author on:
Saphira Kusbandiyah
Saphira Kusbandiyah
b)
1
Department Statistics, Faculty of Mathematics and Sciences, Universitas Brawijaya
, Malang, Indonesia
, 65145
Search for other works by this author on:
a)Corresponding author: [email protected]
AIP Conf. Proc. 2903, 090008 (2023)
Citation
Dwi Ayu Lusia, Saphira Kusbandiyah; Comparing recurrent neural network and long short term memory for forecasting stock price of Moderna Inc. AIP Conf. Proc. 4 October 2023; 2903 (1): 090008. https://doi.org/10.1063/5.0167052
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