A Hybrid LSTM Neural Network Approach for Modeling Periodical Long-Memory Characteristics in Financial Energy Index Time Series

[1] Y. Alyousifi, M. Othman, and A. A. Almohammedi. A novel stochastic fuzzy time series
forecasting model based on a new partition method. IEEE Access, 9:80236–80252, 2021.
[2] T. G. Andersen and T. Bollerslev. Heterogeneous information arrivals and return volatility
dynamics: Uncovering the long-run in high frequency returns. Journal of Finance, 52(3):975–
1005, 1997.
[3] E. Arif, E. Herlinawati, D. Devianto, M. Yollanda, and D. Permana. Hybridization of long
short-term memory neural network in fractional time series modeling of inflation. Frontiers
in Big Data, 6:1282541, 2024.
[4] R. T. Baillie. Long memory processes and fractional integration in econometrics. Journal of
Econometrics, 73(1):5–59, 1996.
[5] T. Bollerslev. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3):307–327, 1986.
[6] T. Bollerslev and H. O. Mikkelsen. Modeling and pricing long memory in stock market
volatility. Journal of Econometrics, 73(1):151–184, 1996.
[7] S. Bordignon, M. Caporin, and F. Lisi. Periodic long memory GARCH models. Econometric
Reviews, 27(5):569–582, 2008.
[8] G. E. P. Box and G. M. Jenkins. Time Series Analysis: Forecasting and Control. Holden-Day,
San Francisco, 1976.
[9] A. H. Bukhari, M. A. Raja, M. Sulaiman, and S. Islam. Fractional neurosequential ARFIMALSTM for financial market forecasting. IEEE Access, 8:71326–71338, 2020.
[10] K. Chen, Z. Chen, S. Wang, and Z. He. Short-term load forecasting with deep residual
networks. IEEE Transactions on Smart Grid, 10(4):3943–3952, 2019.
[11] K. Chen, Y. Zhou, and F. Dai. A LSTM-based hybrid model for financial time series forecasting. Expert Systems with Applications, 190:116261, 2022.
[12] R. Cont. Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2):223–236, 2001.
[13] D. Devianto, K. Ramadani, A. Maiyatri, Y. Asdi, and M. Yollanda. The hybrid model of
autoregressive integrated moving average and fuzzy time series Markov chain on long-memory
data. Frontiers in Applied Mathematics and Statistics, 8:1045241, 2022.
[14] D. Devianto, M. Yollanda, A. Maiyatri, and F. Yanuar. The soft computing FFNN method
for adjusting heteroscedasticity on the time series model of currency exchange rate. Frontiers
in Applied Mathematics and Statistics, 9:1045218, 2023.
[15] D. A. Dickey and W. A. Fuller. Distribution of the estimators for autoregressive time series
with a unit root. Journal of the American Statistical Association, 74(366):427–431, 1979.
[16] R. F. Engle. Autoregressive conditional heteroskedasticity with estimates of the variance of
United Kingdom inflation. Econometrica, 50(4):987–1007, 1982.
[17] T. Fischer and C. Krauss. Deep learning with long short-term memory networks for financial
market predictions. European Journal of Operational Research, 270(2):654–669, 2018.
[18] K. Gajamannage, Y. Park, and D. I. Jayathilake. Real-time forecasting of time series in
financial markets using sequentially trained dual-LSTMs. Expert Systems with Applications,
223:119879, 2023.
[19] J.-C. Gamboa. Deep learning for time-series analysis. arXiv preprint arXiv:1701.01887,
2017.
[20] L. R. Glosten, R. Jagannathan, and D. E. Runkle. On the relation between the expected
value and the volatility of the nominal excess return on stocks. The Journal of Finance,
48(5):1779–1801, 1993.
[21] B. Gulmez. Stock price prediction with optimized deep LSTM network with artificial rabbits
optimization algorithm. Expert Systems with Applications, 227:120346, 2023.
[22] S. A. Haider, S. R. Naqvi, T. Akram, G. A. Umar, A. Shahzad, M. R. Sial, et al. LSTM
neural network based forecasting model for wheat production in Pakistan. Agronomy, 9:72,
2019.
[23] T. Hasenzagl, F. Pellegrino, L. Reichlin, and G. Ricco. A model of the Fed’s view on inflation.
Economic Research Paper, 104:686–704, 2022.
[24] S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Computation, 9(8):1735–
1780, 1997.
[25] H. H. Huang, N. H. Chan, K. Chen, and C. K. Ing. Consistent order selection for ARFIMA
processes. Annals of Statistics, 50:1297–1319, 2022.
[26] C. M. Jarque and A. K. Bera. A test for normality of observations and regression residuals.
International Statistical Review, 55(2):163–172, 1987.
[27] M. E. Javanmard, Y. Tang, Z. Wang, and P. Tontiwachwuthikul. Forecast energy demand,
CO2 emissions and energy resource impacts for the transportation sector. Applied Energy,
338:120830, 2023.
[28] S. Kaushik, A.Choudhury, P. K. Sheron, N. Dasgupta, S. Natarajan, L. A. Pickett, et al.
AI in healthcare: time-series forecasting using statistical, neural, and ensemble architectures.
Frontiers in Big Data, 3:4, 2020.
[29] H. Y. Kim and C. H. Won. Forecasting the volatility of stock price index: A hybrid model
integrating LSTM with multiple GARCH-type models. Expert Systems with Applications,
103:25–37, 2021.
[30] B. Lei, Z. Liu, and Y. Song. On stock volatility forecasting based on text mining and deep
learning under high-frequency data. Journal of Forecasting, 40(8):1596–1610, 2021.
[31] R. Liu, Y. Jiang, and J. Lin. Forecasting the volatility of specific risk for stocks with LSTM.
Procedia Computer Science, 202:111–114, 2022.
[32] G. M. Ljung and G. E. P. Box. On a measure of lack of fit in time series models. Biometrika,
65(2):297–303, 1978.
[33] M. McAleer. Automated inference and learning in modeling volatility. Econometric Theory,
21(1):232–261, 2005.
[34] D. B. Nelson. Conditional heteroskedasticity in asset returns: A new approach. Econometrica,
59(2):347–370, 1991.
[35] D. M. Nelson, A. C. M. Pereira, and R. A. de Oliveira. Stock market’s price movement prediction with LSTM neural networks. In International Joint Conference on Neural Networks
(IJCNN), pages 1–6, 2018.
[36] S. Pan, S. Long, Y. Wang, and Y. Xie. Nonlinear asset pricing in Chinese stock market: a
deep learning approach. International Review of Financial Analysis, 87:102627, 2023.
[37] M. L. Rahman, M. Islam, and M. Roy. Modeling inflation in Bangladesh. Open Journal of
Statistics, 10:998–1009, 2020.
[38] E. Ramos-P´erez, P. J. Alonso-Gonz´alez, and J. J. N´u˜nez-Vel´azquez. Multi-transformer:
A new neural network-based architecture for forecasting S&P volatility. Mathematics,
9(15):1794, 2021.
[39] A. Shewalkar, D. Nyavanandi, and S. Ludwig. Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU. Journal of Artificial Intelligence
and Soft Computing Research, 9:235–245, 2019.
[40] Y. Wang and L. Liu. A hybrid forecasting model based on LSTM and GARCH for financial
time series. Applied Sciences, 9(3):550, 2019.
[41] J. Wu, N. Levi, R. Araujo, and Y.-G. Wang. An evaluation of the impact of COVID-19
lockdowns on electricity demand. Electric Power Systems Research, 216:109015, 2023.
[42] X. Xu, Y. Zhang, C. A. McGrory, J. Wu, and Y.-G. Wang. Forecasting stock closing prices
with an application to airline company data. Data Science and Management, 6:239–246,
2023.
[43] Y. Yang, H. Zhou, J. Wu, Z. Ding, Y.-C. Tian, D. Yue, et al. Robust adaptive rescaled lncosh
neural network regression toward time-series forecasting. IEEE Transactions on Systems,
Man, and Cybernetics, 53:5658–5669, 2023.
[44] L. Yu, S. Wang, and K. K. Lai. Financial time series forecasting with nonlinear and nonstationary characteristics: A review. Applied Soft Computing, 95:106545, 2020.
[45] Y. Yu, X. Si, C. Hu, and J. Zhang. A review of recurrent neural networks: LSTM cells and
network architectures. Neural Computation, 31:1235–1270, 2019.
[46] J. Zhao, S. Xu, and X. Zhang. Periodic volatility modeling with hybrid neural networks and
GARCH-type models. Physica A, 616:128574, 2023.