Transfer Learning Implementation on Bi-LSTM with Optimizer to Improve Non-Ferrous Metal Prices Prediction

Adji Pratomo, Muhammad O. Jatmika, Bedine Kerim, Rahmat Budiarto

Abstract


Over the past few years, the implementation of renewable energy or go-green has intensified along with the rapid development of its technology and increasing uncertainty of natural conditions that cause the prices of non-ferrous metals such as copper, aluminum, nickel, etc. used as main components for developing renewable energy devices, e.g.: battery, experience instability price in the commodity futures market. Economic players who trade metals in the futures market certainly need to be careful and must evaluate the state of the world economy. This study proposes a prediction engine as a combination of Bidirectional Long-Short Term Memory (BiLSTM), with three optimization algorithms, i.e.: Adam, Root Mean Squared Propagation (RMSProp), and Stochastic Gradient Descent (SGD), and transfer learning to make model training better. Experiments on four historical data on nickel, lead, aluminum and copper prices in the commodity futures market are conducted. The selected features are: open price, close price and volume price. Twelve models will be created to find the model that best predicts the metal prices. The top 3 models with the best performance were selected, they are: model 4 RMSProp with R2 value of 0,99029 and MSE 0,00076 as the first ranking, model 3 Adam with R2 value of 0,98877 and MSE 0,00074 as the second ranking, and model 4 Adam with value of R2 0,98522 and MSE 0,00115 as the third ranking.

Keywords


Metals commodity; Price Prediction; BiLSTM; Optimizer Waterfall; Transfer Learning

Full Text:

PDF

References


G. Schuh, G. Reinhart, J. Prote, F. Sauermann, J. Horsthofer, F. Oppolzer, F., & D. Knoll. “Data mining definitions and applications for the management of production complexity,” Procedia CIRP, vol. 81, pp. 874–879. https://doi.org/10.1016/j.procir.2019.03.217.

Dedi, & A.Cherid, “Data mining data processing prospective of Indonesian migrant workers (PMI) with application of K-Means clustering and K-nearest neighbor (KNN) classification: case study of PT. SAM,” B.Sc Thesis, Mercu Buana University, Indonesia. https://repository.mercubuana.ac.id/61695/

R. Law, G. Li, D.K.C Fong & X. Han, “Tourism demand forecasting: A deep learning approach,” Annals of Tourism Research., 75, 410–423. https://doi.org/10.1016/j.annals.2019.01.014.

J.M. Stokes, K. Yang, S. Kyle, W. Jin, A. Cubillos-Ruiz, N.M. Donghia, C.R. MacNair, S. French, L.A Carfrae, Z. Bloom-Ackermann, V.M. Tran, A. Chiappino-Pepe, A.H. Badran, I.W. Andrews, E.J. Chory, G.M. Church, E.D. Brown, T.S. Jaakkola, R. Barzilay & J. C. Collins, “A deep learning approach to antibiotic discovery,” Cell, vol. 180, pp. 688 – 702, 2020. https://doi.org/10.1016/j.cell.2020.01.021

C.Xu, J. Ji & P. Liu, “The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets,” Transportation Research Part C: Emerging Technologies, vol. 95, ppl 47–60. https://doi.org/10.1016/j.trc.2018.07.013.

O.I Abiodun, A. Jantan, A.E. Omolara, K.V. Dada, N.A.E. Mohamed & H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” Heliyon, vol. 4, e00938. https://doi.org/10.1016/j.heliyon.2018.e00938.

D. Rosadi, D. Arisanty & D. Agustina, “Prediction of forest fire using neural networks with backpropagation learning and exreme learning machine approach using meteorological and weather index variables,” Media Statistika., vol. 14, ppl 118–124, 2022. https://doi.org/10.14710/medstat.14.2.118-124

I.T. Rahayu, N. Nurhasanah & R. Adriat, “Prediction of dengue hemorrhagic fever cases based on weather parameters using back propagation neural networks (case study in Pontianak city),” Jurnal Pendidikan Fisika Indonesia, vol. 15, pp. 114–121, 2019. https://doi.org/10.15294/jpfi.v15i2.19633.

P. Jiang, Z. Liu, X. Niu& L. Zhang, “A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting,” Energy, vol. 217, 119361. https://doi.org/10.1016/j.energy.2020.119361.

I. Kurniawan, L.S. Silaban& D. Munandar, “Implementation of convolutional neural network and multilayer perceptron in predicting air temperature in Padang,” RESTI, vol. 4, pp. 2–7, 2020. https://doi.org/10.29207/resti.v4i6.2456.

R.F. Rahmat, Dennis, O.S. Sitompul, S. Purnamawati & R. Budiarto, “Advertisement billboard detection and geotagging system with inductive transfer learning in deep convolutional neural network” Telkomnika, vol. 17, pp. 2659–2666, 2019. http://doi.org/10.12928/telkomnika.v17i5.11276.

F. Makinoshima, Y. Oishi, T. Yamazaki, T. Furumura& F. Imamura. “Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks,” Nature Communications, vol. 12, pp. 1–10, 2021. https://doi.org/10.1038/s41467-021-22348-0.

M.A. Faishol, E. Endroyono, & A. N. Irfansyah, “Predict urban air pollution in surabaya using recurrent neural network – long short term memory,” JUTI, vol. 18, no. 102, 2020. http://dx.doi.org/10.12962/j24068535.v18i2.a988.

F. Granata & F. D. Nunno, “Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks,” Agriculture and Water Management, vol. 255, 107040, 2021. https://doi.org/10.1016/j.agwat.2021.107040.

K.E. ArunKumar, D.V. Kalaga, C.M.S. Kumar, M. Kawaji, M & T. M. Brenza, “Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and long short-term memory (LSTM) cells,” Chaos, Solitons & Fractals, vol. 146, 110861, 2021. https://doi.org/10.1016/j.chaos.2021.110861.

T. Ahmad, & H. Chen, “A review on machine learning forecasting growth trends and their real-time applications in different energy systems,” Sustainable Cities and Society, vol. 54, 102010, 2020. https://doi.org/10.1016/j.scs.2019.102010.

H.A. Khoshalan, J. Shakeri, I. Najmoddini & M. Asadizadeh, “Forecasting copper price by application of robust artificial intelligence techniques,” Resources Policy, vol. 73, 102239, 2021. https://doi.org/10.1016/j.resourpol.2021.102239.

N. Afrianto, D.H. Fudholi & S. Rani. « Stock price prediction using BiLSTM with public sentiment factor,” RESTI, vol. 6, ppl 41–46, 2022. doi: 10.29207/resti.v6i1.3676.

F.D. Adhinata & D. P. Rakhmadani, “Prediction of Covid-19 daily case in indonesia using long short term memory method. Teknika, vol. 10, ppl 62–67, 2021. https://doi.org/10.34148/teknika.v10i1.328.

A.W. Ramadhan, D. Adytia, D. Saepudin, S. Husrin & A. Adiwijaya, ”Forecasting of sea level time series using RNN and LSTM case study in Sunda strait,” Lontar Komputer, vol. 12, no. 130, 2021. https://doi.org/10.24843/LKJITI.2022.v13.i02

K.U. Jaseena & B. C. Kovoor, “Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks,” Energy Conversion and Management, vol. 234, 113944, 2021. https://doi.org/10.1016/j.enconman.2021.113944.

A. Koshiyama, S. Flennerhag, S.B. Blumberg, N. Firoozye& P. Treleaven, “QuantNet: transferring learning across systematic trading strategies, 2020. http://arxiv.org/abs/2004.03445.

R. Ye & Q. Dai, “A novel transfer learning framework for time series forecasting,” Knowledge-Based System, vol. 156, pp. 74–99, 2018. https://doi.org/10.1016/j.knosys.2018.05.021.

A. Mustafid, M.M. Pamuji, & S. Helmiyah,. “A comparative study of transfer learning and fine-tuning method on deep learning models for wayang dataset classification,” IJID, vol. 9, pp. 100–110, 2020. https://doi.org/10.14421/ijid.2020.09207.

Y. Gao, Y. Ruan, C. Fang & S. Yin. “Deep learning and transfer learning models of energy consumption forecasting for a building with poor information data,” Energy and Buildings, vol. 223, 110156, 2020. https://doi.org/10.1016/j.enbuild.2020.110156.

M.S. Acharya, A. Armaan& A. S. Antony, “A comparison of regression models for prediction of graduate admissions. ICCIDS 2019, pp. 1–5, 2019. https://doi.org/10.1109/ICCIDS.2019.8862140.

J. Kumar, R. Goomer & A. K. Singh. “Long short term memory recurrent neural network (LSTM-RNN) based workload forecasting model for cloud datacenters,” Procedia Computer Science, vol. 125, pp. 676–682, 2018. https://doi.org/10.1016/j.procs.2017.12.087.

B. Shao, M. Li, Y. Zhao & G. Bian, “Nickel price forecast based on the lstm neural network optimized by the improved pso algorithm,” Mathematical Problems in Engineering, 2019, Article ID 1934796. https://doi.org/10.1155/2019/1934796.

L.L. Lu, X. Ma, Y.X. Wang, & G. B. Yu, « Lead price forecasting based on ARIMA model,” Advanced Materials Research, vol. 488-489, pp. 1582-1586, 2012.

S.J. Mysen & E. M. Thornton, “Forecasting the price of aluminum using machine learning empirical comparison of machine learning and statistical methods,” 2021, Master thesis, Norwegian School of Economics. https://openaccess.nhh.no/nhh-xmlui/bitstream/handle/11250/2985394/masterthesis.pdf?sequence=1.




DOI: http://dx.doi.org/10.22441/collabits.v1i1.25468

Refbacks

  • There are currently no refbacks.


Collabits Journal
Portal ISSNPrint ISSN: 3062-8601
Online ISSN: 3046-6709

Sekretariat
Fakultas Ilmu Komputer
Universitas Mercu Buana
Jl. Raya Meruya Selatan, Kembangan, Jakarta 11650
Tlp./Fax: +62215871335

http://publikasi.mercubuana.ac.id/index.php/collabits

e-mail: [email protected]

Creative Commons Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.