References
[1]. G. Box and G. Jenkins, “Models for Forecasting Seasonal and Non-seasonal Time Series,” Defense Technical Information Center (DTIC), AD0656685, 1967. DOI:10.1016/S0167-5648(08)70667-1.
[2]. A. A. Ariyo, A. O. Adewumi and C. K. Ayo, “Stock Price Prediction Using the ARIMA Model,” in 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, pp. 106-112, 2014. DOI: 10.1109/UKSim.2014.67.
[3]. B. Henrique, V. Sobreiro and H. Kimura, “Stock price prediction using support vector regression on daily and up to the minute prices,” The Journal of Finance and Data Science, vol. 4, no. 3, pp. 183-201, 2018. DOI: 10.1016/j.jfds.2018.04.003.
[4]. J. Liu, X. He, J. Gao and J. Han, “Stock price prediction using LSTM, RNN and CNN sl,” in ICACCI, pp. 230-234, 2017. DOI: 10.1109/ICACCI.2017.8126078.
[5]. E. Hoseinzade and S. Haratizadeh, “CNNpred: CNN based stock market prediction using a diverse set of variables,” Expert Systems with Applications, vol. 129, pp. 273-285, 2019. DOI: 10.1016/j.eswa.2019.03.029.
[6]. A.Vaswani et al., “Attention is all you need,” in Advances in Neural Information Processing Systems 30 (NIPS), I.Guyon et al., Eds., pp.5998–6008, 2017. DOI: 10.48550/arXiv:1706:03762.
[7]. H.Y.Zhou et al., “Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting,” arXiv preprint arXiv:2012:07436, 2021. DOI:10:48550/arXiv:2012:07436.
[8]. M.Chen et al., “AutoFormer: Searching Transformers for Visual Recognition,” arXiv preprint arXiv:2107:00651, 2021. DOI: 10:48550/arXiv:2107:00651.
[9]. Z.Wang et al., “Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting,” arXiv preprint arXiv:2205:14415, 2022. DOI: 10:48550/arXiv:2205:14415.
[10]. D.P.Kingma and J.Ba., “Adam:A method for stochastic optimization,” in Proceedings of the 3rd International Conference for Learning Representations(ICLR), pp1-15, 2015. DOI: 10:48550/arXiv1412:6980.
Cite this article
Wu,Y. (2023). Comparison between transformer, informer, autoformer and non-stationary transformer in financial market. Applied and Computational Engineering,29,68-78.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. G. Box and G. Jenkins, “Models for Forecasting Seasonal and Non-seasonal Time Series,” Defense Technical Information Center (DTIC), AD0656685, 1967. DOI:10.1016/S0167-5648(08)70667-1.
[2]. A. A. Ariyo, A. O. Adewumi and C. K. Ayo, “Stock Price Prediction Using the ARIMA Model,” in 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, pp. 106-112, 2014. DOI: 10.1109/UKSim.2014.67.
[3]. B. Henrique, V. Sobreiro and H. Kimura, “Stock price prediction using support vector regression on daily and up to the minute prices,” The Journal of Finance and Data Science, vol. 4, no. 3, pp. 183-201, 2018. DOI: 10.1016/j.jfds.2018.04.003.
[4]. J. Liu, X. He, J. Gao and J. Han, “Stock price prediction using LSTM, RNN and CNN sl,” in ICACCI, pp. 230-234, 2017. DOI: 10.1109/ICACCI.2017.8126078.
[5]. E. Hoseinzade and S. Haratizadeh, “CNNpred: CNN based stock market prediction using a diverse set of variables,” Expert Systems with Applications, vol. 129, pp. 273-285, 2019. DOI: 10.1016/j.eswa.2019.03.029.
[6]. A.Vaswani et al., “Attention is all you need,” in Advances in Neural Information Processing Systems 30 (NIPS), I.Guyon et al., Eds., pp.5998–6008, 2017. DOI: 10.48550/arXiv:1706:03762.
[7]. H.Y.Zhou et al., “Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting,” arXiv preprint arXiv:2012:07436, 2021. DOI:10:48550/arXiv:2012:07436.
[8]. M.Chen et al., “AutoFormer: Searching Transformers for Visual Recognition,” arXiv preprint arXiv:2107:00651, 2021. DOI: 10:48550/arXiv:2107:00651.
[9]. Z.Wang et al., “Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting,” arXiv preprint arXiv:2205:14415, 2022. DOI: 10:48550/arXiv:2205:14415.
[10]. D.P.Kingma and J.Ba., “Adam:A method for stochastic optimization,” in Proceedings of the 3rd International Conference for Learning Representations(ICLR), pp1-15, 2015. DOI: 10:48550/arXiv1412:6980.