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Gold Price Forecasting Using LSTM, Bi-LSTM and GRU

Yıl 2021, Sayı: 31, 341 - 347, 31.12.2021
https://doi.org/10.31590/ejosat.959405

Öz

Due to the multifactorial and non-linear nature of the gold market, it is difficult to predict the gold price. The gold price is affected by many external factors, such as market environment, economic crises, oil price increases, tax advantages and interest rates. Therefore, multivariate models can better predict the gold price than univariate models. This study investigated the effects of gold price, crude oil price, exchange rate index, stock market index, and interest indicators between 2001 and 2021. Models created using LSTM, Bi-LSTM and GRU methods were evaluated using lowest Root Mean Square Error (RMSE), Mean Absolute Percent Error (MAPE) and Mean Absolute Error (MAE) metrics. The LSTM model performed best, with 3.48 MAPE, 61,728 RMSE and 48.85 MAE values.

Kaynakça

  • Alameer, Z., Abd Elaziz, M., Ewees, A. A., Ye, H., & Jianhua, Z. (2019). Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm. Resources Policy, 61, 250-260.
  • Alpay, Ö. (2020). LSTM Mimarisi Kullanarak USD/TRY Fiyat Tahmini. Avrupa Bilim ve Teknoloji Dergisi, Ejosat Özel Sayı 2020 (ARACONF) , 452-456.
  • Aygun, B., Kabakcı Gunay, E. (2021). Comparison of Statistical and Machine Learning Algorithms for Forecasting Daily Bitcoin Returns . Avrupa Bilim ve Teknoloji Dergisi , (21) , 444-454.
  • Bank for International Settlements, Real Broad Effective Exchange Rate for United States [RBUSBIS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/RBUSBIS, June 28, 2021.
  • Board of Governors of the Federal Reserve System (US), Effective Federal Funds Rate [FEDFUNDS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/FEDFUNDS, June 28, 2021
  • Beckmann, J., & Czudaj, R. (2013). Gold as an inflation hedge in a time-varying coefficient framework. The North American Journal of Economics and Finance, 24, 208-222.
  • Chen, R., & Xu, J. (2019). Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model. Energy Economics, 78, 379-391.
  • Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
  • Dar, A. B., & Maitra, D. (2017). Is gold a weak or strong hedge and safe haven against stocks? Robust evidences from three major gold-consuming countries. Applied Economics, 49(53), 5491-5503.
  • Du, S., Li, T., Yang, Y., & Horng, S. J. (2020). Multivariate time series forecasting via attention-based encoder–decoder framework. Neurocomputing.
  • Dutta, A., Kumar, S., & Basu, M. (2020). A gated recurrent unit approach to bitcoin price prediction. Journal of Risk and Financial Management, 13(2), 23.
  • Erb, C. B., & Harvey, C. R. (2013). The golden dilemma. Financial Analysts Journal, 69(4), 10-42.
  • Gangopadhyay, K., Jangir, A., & Sensarma, R. (2016). Forecasting the price of gold: An error correction approach. IIMB management review, 28(1), 6-12.
  • Ghosh, D., Levin, E. J., Macmillan, P., & Wright, R. E. (2004). Gold as an inflation hedge?. Studies in Economics and Finance.
  • Giannellis, N., & Koukouritakis, M. (2019). Gold price and exchange rates: A panel smooth transition regression model for the G7 countries. The North American Journal of Economics and Finance, 49, 27-46.
  • Gokmenoglu, K. K., & Fazlollahi, N. (2015). The interactions among gold, oil, and stock market: Evidence from S&P500. Procedia Economics and Finance, 25, 478-488.
  • Gunduz, H. (2021). An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination. Financial Innovation, 7(1), 1-24.
  • Güleryüz, D., Özden, E. (2020). The Prediction of Brent Crude Oil Trend Using LSTM and Facebook Prophet. Avrupa Bilim ve Teknoloji Dergisi , (20) , 1-9. DOI: 10.31590/ejosat.759302
  • Hassani, H., Silva, E. S., Gupta, R., & Segnon, M. K. (2015). Forecasting the price of gold. Applied Economics, 47(39), 4141-4152.
  • Hochreiter, S., & Urgen Schmidhuber, J. J. (1997). Long short term memory. Neural computation. MEMORY Neural Computation.
  • Index Mundi, Gold Monthly Price- US Dollars per Troy Ounce, retrieved from https://www.indexmundi.com/commodities/?commodity=gold&months=120, June 28, 2021
  • Jianwei, E., Ye, J., & Jin, H. (2019). A novel hybrid model on the prediction of time series and its application for the gold price analysis and forecasting. Physica A: Statistical Mechanics and its Applications, 527, 121454.
  • Karim, F., Majumdar, S., Darabi, H., & Harford, S. (2019). Multivariate LSTM-FCNs for time series classification. Neural Networks.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
  • Li, B. (2014). Research on WNN modeling for gold price forecasting based on improved artificial bee colony algorithm. Computational intelligence and neuroscience, 2014.
  • Liu, D., & Li, Z. (2017). Gold price forecasting and related influence factors analysis based on random forest. In Proceedings of the Tenth International Conference on Management Science and Engineering Management (pp. 711-723). Springer, Singapore.
  • Livieris, I. E., Pintelas, E., & Pintelas, P. (2020). A CNN–LSTM model for gold price time-series forecasting. Neural computing and applications, 32(23), 17351-17360.
  • Mensi, W., Beljid, M., Boubaker, A., & Managi, S. (2013). Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold. Economic Modelling, 32, 15-22.
  • Munkhdalai, L., Munkhdalai, T., Park, K. H. O., Amarbayasgalan, T., Batbaatar, E., Park, H. W. O. O., & Ryu, K. H. (2019). An end-to-end adaptive input selection with dynamic weights for forecasting multivariate time series. IEEE Access.
  • Nguyen, H. D., Tran, K. P., Thomassey, S., & Hamad, M. (2021). Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management. International Journal of Information Management
  • Olah, Christopher. (2015) Understanding LSTM Networks [Blog post]. Retrieved from http://colah.github.io/posts/2015-08-UnderstandingLSTMs/
  • Organization for Economic Co-operation and Development. (2021, Feb. 4). Consumer Price Index: Total All Items for the United States [CPALTT01USM657N], Federal Reserve Bank of St. Louis, 2021. [Online]. Available: https://fred.stlouisfed.org/series/CPALTT01USM657N
  • Parisi, A., Parisi, F., & Díaz, D. (2008). Forecasting gold price changes: Rolling and recursive neural network models. Journal of Multinational financial management, 18(5), 477-487
  • Parmezan, A. R. S., Souza, V. M. A., & Batista, G. E. A. P. A. (2019). Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. Information Sciences.
  • Risse, M. (2019). Combining wavelet decomposition with machine learning to forecast gold returns. International Journal of Forecasting, 35(2), 601-615.
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
  • Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE transactions on Signal Processing, 45(11), 2673-2681.
  • Shafiee, S., & Topal, E. (2010). An overview of global gold market and gold price forecasting. Resources policy, 35(3), 178-189.
  • Shen, G., Tan, Q., Zhang, H., Zeng, P., & Xu, J. (2018). Deep learning with gated recurrent unit networks for financial sequence predictions. Procedia computer science, 131, 895-903.
  • Shen, Z., Zhang, Y., Lu, J., Xu, J., & Xiao, G. (2020). A novel time series forecasting model with deep learning. Neurocomputing.
  • Sivalingam, K. C., Mahendran, S., & Natarajan, S. (2016). Forecasting gold prices based on extreme learning machine. International Journal of Computers Communications & Control, 11(3), 372-380.
  • U.S. Energy Information Administration. (2021, Feb. 4). Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma [DCOILWTICO], Federal Reserve Bank of St. Louis, 2021. [Online].Available: https://fred.stlouisfed.org/series/DCOILWTICO
  • Xian, L., He, K., & Lai, K. K. (2016). Gold price analysis based on ensemble empirical model decomposition and independent component analysis. Physica A: Statistical Mechanics and its Applications, 454, 11-23.
  • Wang, Y. S., & Chueh, Y. L. (2013). Dynamic transmission effects between the interest rate, the US dollar, and gold and crude oil prices. Economic Modelling, 30, 792-798.
  • Weng, F., Chen, Y., Wang, Z., Hou, M., Luo, J., & Tian, Z. (2020). Gold price forecasting research based on an improved online extreme learning machine algorithm. Journal of Ambient Intelligence and Humanized Computing, 1-11.
  • Yahoo Finance, S&P500, Retrieved from https://finance.yahoo.com, June 28, 2021
  • Yazdani-Chamzini, A., Yakhchali, S. H., Volungevičienė, D., & Zavadskas, E. K. (2012). Forecasting gold price changes by using adaptive network fuzzy inference system. Journal of Business Economics and Management, 13(5), 994-1010.
  • Yıldırım, D. C., Toroslu, I. H., & Fiore, U. (2021). Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators. Financial Innovation, 7(1), 1-36.
  • Zhang, P. (2003). Zhang, G.P.: Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing 50, 159-175. Neurocomputing.
  • Zhang, P., & Ci, B. (2020). Deep belief network for gold price forecasting. Resources Policy, 69, 101806.

LSTM, Bi-LSTM ve GRU ile Altın Fiyatı Tahmini

Yıl 2021, Sayı: 31, 341 - 347, 31.12.2021
https://doi.org/10.31590/ejosat.959405

Öz

Altın piyasasının çok faktörlü ve doğrusal olmayan özelliği nedeniyle altın fiyatının tahminini yapmak zordur. Altın fiyatı, piyasa ortamı, ekonomik kriz, petrol fiyatlarındaki artış, vergi avantajları, faiz oranları gibi dış faktörlerden etkilenmektedir. Bu nedenle çok değişkenli modeller altın fiyatını tahmin etmede daha iyi sonuçlar verebilmektedir. Makalede, 2001–2021 yılları arasında altın fiyatı, ham petrol fiyatı, döviz kuru endeksi, borsa endeksi ve faiz göstergeleri kullanılmıştır. LSTM, Bi-LSTM ve GRU yöntemleri kullanılarak oluşturulan modeller en düşük Kök Ortalama Kare Hata (RMSE), Ortalama Mutlak Yüzde Hata (MAPE) ve Ortalama Mutlak Hata (MAE) metrikleri ile değerlendirilmiştir. LSTM modeli 3,48 MAPE, 61,728 RMSE ve 48,85 MAE değerleri ile en iyi yöntem olmuştur.

Kaynakça

  • Alameer, Z., Abd Elaziz, M., Ewees, A. A., Ye, H., & Jianhua, Z. (2019). Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm. Resources Policy, 61, 250-260.
  • Alpay, Ö. (2020). LSTM Mimarisi Kullanarak USD/TRY Fiyat Tahmini. Avrupa Bilim ve Teknoloji Dergisi, Ejosat Özel Sayı 2020 (ARACONF) , 452-456.
  • Aygun, B., Kabakcı Gunay, E. (2021). Comparison of Statistical and Machine Learning Algorithms for Forecasting Daily Bitcoin Returns . Avrupa Bilim ve Teknoloji Dergisi , (21) , 444-454.
  • Bank for International Settlements, Real Broad Effective Exchange Rate for United States [RBUSBIS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/RBUSBIS, June 28, 2021.
  • Board of Governors of the Federal Reserve System (US), Effective Federal Funds Rate [FEDFUNDS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/FEDFUNDS, June 28, 2021
  • Beckmann, J., & Czudaj, R. (2013). Gold as an inflation hedge in a time-varying coefficient framework. The North American Journal of Economics and Finance, 24, 208-222.
  • Chen, R., & Xu, J. (2019). Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model. Energy Economics, 78, 379-391.
  • Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
  • Dar, A. B., & Maitra, D. (2017). Is gold a weak or strong hedge and safe haven against stocks? Robust evidences from three major gold-consuming countries. Applied Economics, 49(53), 5491-5503.
  • Du, S., Li, T., Yang, Y., & Horng, S. J. (2020). Multivariate time series forecasting via attention-based encoder–decoder framework. Neurocomputing.
  • Dutta, A., Kumar, S., & Basu, M. (2020). A gated recurrent unit approach to bitcoin price prediction. Journal of Risk and Financial Management, 13(2), 23.
  • Erb, C. B., & Harvey, C. R. (2013). The golden dilemma. Financial Analysts Journal, 69(4), 10-42.
  • Gangopadhyay, K., Jangir, A., & Sensarma, R. (2016). Forecasting the price of gold: An error correction approach. IIMB management review, 28(1), 6-12.
  • Ghosh, D., Levin, E. J., Macmillan, P., & Wright, R. E. (2004). Gold as an inflation hedge?. Studies in Economics and Finance.
  • Giannellis, N., & Koukouritakis, M. (2019). Gold price and exchange rates: A panel smooth transition regression model for the G7 countries. The North American Journal of Economics and Finance, 49, 27-46.
  • Gokmenoglu, K. K., & Fazlollahi, N. (2015). The interactions among gold, oil, and stock market: Evidence from S&P500. Procedia Economics and Finance, 25, 478-488.
  • Gunduz, H. (2021). An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination. Financial Innovation, 7(1), 1-24.
  • Güleryüz, D., Özden, E. (2020). The Prediction of Brent Crude Oil Trend Using LSTM and Facebook Prophet. Avrupa Bilim ve Teknoloji Dergisi , (20) , 1-9. DOI: 10.31590/ejosat.759302
  • Hassani, H., Silva, E. S., Gupta, R., & Segnon, M. K. (2015). Forecasting the price of gold. Applied Economics, 47(39), 4141-4152.
  • Hochreiter, S., & Urgen Schmidhuber, J. J. (1997). Long short term memory. Neural computation. MEMORY Neural Computation.
  • Index Mundi, Gold Monthly Price- US Dollars per Troy Ounce, retrieved from https://www.indexmundi.com/commodities/?commodity=gold&months=120, June 28, 2021
  • Jianwei, E., Ye, J., & Jin, H. (2019). A novel hybrid model on the prediction of time series and its application for the gold price analysis and forecasting. Physica A: Statistical Mechanics and its Applications, 527, 121454.
  • Karim, F., Majumdar, S., Darabi, H., & Harford, S. (2019). Multivariate LSTM-FCNs for time series classification. Neural Networks.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
  • Li, B. (2014). Research on WNN modeling for gold price forecasting based on improved artificial bee colony algorithm. Computational intelligence and neuroscience, 2014.
  • Liu, D., & Li, Z. (2017). Gold price forecasting and related influence factors analysis based on random forest. In Proceedings of the Tenth International Conference on Management Science and Engineering Management (pp. 711-723). Springer, Singapore.
  • Livieris, I. E., Pintelas, E., & Pintelas, P. (2020). A CNN–LSTM model for gold price time-series forecasting. Neural computing and applications, 32(23), 17351-17360.
  • Mensi, W., Beljid, M., Boubaker, A., & Managi, S. (2013). Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold. Economic Modelling, 32, 15-22.
  • Munkhdalai, L., Munkhdalai, T., Park, K. H. O., Amarbayasgalan, T., Batbaatar, E., Park, H. W. O. O., & Ryu, K. H. (2019). An end-to-end adaptive input selection with dynamic weights for forecasting multivariate time series. IEEE Access.
  • Nguyen, H. D., Tran, K. P., Thomassey, S., & Hamad, M. (2021). Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management. International Journal of Information Management
  • Olah, Christopher. (2015) Understanding LSTM Networks [Blog post]. Retrieved from http://colah.github.io/posts/2015-08-UnderstandingLSTMs/
  • Organization for Economic Co-operation and Development. (2021, Feb. 4). Consumer Price Index: Total All Items for the United States [CPALTT01USM657N], Federal Reserve Bank of St. Louis, 2021. [Online]. Available: https://fred.stlouisfed.org/series/CPALTT01USM657N
  • Parisi, A., Parisi, F., & Díaz, D. (2008). Forecasting gold price changes: Rolling and recursive neural network models. Journal of Multinational financial management, 18(5), 477-487
  • Parmezan, A. R. S., Souza, V. M. A., & Batista, G. E. A. P. A. (2019). Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. Information Sciences.
  • Risse, M. (2019). Combining wavelet decomposition with machine learning to forecast gold returns. International Journal of Forecasting, 35(2), 601-615.
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
  • Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE transactions on Signal Processing, 45(11), 2673-2681.
  • Shafiee, S., & Topal, E. (2010). An overview of global gold market and gold price forecasting. Resources policy, 35(3), 178-189.
  • Shen, G., Tan, Q., Zhang, H., Zeng, P., & Xu, J. (2018). Deep learning with gated recurrent unit networks for financial sequence predictions. Procedia computer science, 131, 895-903.
  • Shen, Z., Zhang, Y., Lu, J., Xu, J., & Xiao, G. (2020). A novel time series forecasting model with deep learning. Neurocomputing.
  • Sivalingam, K. C., Mahendran, S., & Natarajan, S. (2016). Forecasting gold prices based on extreme learning machine. International Journal of Computers Communications & Control, 11(3), 372-380.
  • U.S. Energy Information Administration. (2021, Feb. 4). Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma [DCOILWTICO], Federal Reserve Bank of St. Louis, 2021. [Online].Available: https://fred.stlouisfed.org/series/DCOILWTICO
  • Xian, L., He, K., & Lai, K. K. (2016). Gold price analysis based on ensemble empirical model decomposition and independent component analysis. Physica A: Statistical Mechanics and its Applications, 454, 11-23.
  • Wang, Y. S., & Chueh, Y. L. (2013). Dynamic transmission effects between the interest rate, the US dollar, and gold and crude oil prices. Economic Modelling, 30, 792-798.
  • Weng, F., Chen, Y., Wang, Z., Hou, M., Luo, J., & Tian, Z. (2020). Gold price forecasting research based on an improved online extreme learning machine algorithm. Journal of Ambient Intelligence and Humanized Computing, 1-11.
  • Yahoo Finance, S&P500, Retrieved from https://finance.yahoo.com, June 28, 2021
  • Yazdani-Chamzini, A., Yakhchali, S. H., Volungevičienė, D., & Zavadskas, E. K. (2012). Forecasting gold price changes by using adaptive network fuzzy inference system. Journal of Business Economics and Management, 13(5), 994-1010.
  • Yıldırım, D. C., Toroslu, I. H., & Fiore, U. (2021). Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators. Financial Innovation, 7(1), 1-36.
  • Zhang, P. (2003). Zhang, G.P.: Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing 50, 159-175. Neurocomputing.
  • Zhang, P., & Ci, B. (2020). Deep belief network for gold price forecasting. Resources Policy, 69, 101806.
Toplam 50 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Mustafa Yurtsever 0000-0003-2232-0542

Yayımlanma Tarihi 31 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Sayı: 31

Kaynak Göster

APA Yurtsever, M. (2021). Gold Price Forecasting Using LSTM, Bi-LSTM and GRU. Avrupa Bilim Ve Teknoloji Dergisi(31), 341-347. https://doi.org/10.31590/ejosat.959405