Forecasting Time Series Data using Recurrent Neural Networks: A Systematic Review

Manpreet Kaur Bhatia; Vinayak Bhatt

doi:10.55544/jrasb.3.6.22

Authors

Manpreet Kaur Bhatia Assistant Professor, Data Science, Asian School of Media Studies, Noida, U.P., INDIA
Vinayak Bhatt Digital Analytics Specialist, BT Group, Gurugram, Haryana, INDIA

DOI:

https://doi.org/10.55544/jrasb.3.6.22

Keywords:

Time series forecasting, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), deep learning, hybrid models, attention mechanisms, machine learning, statistical methods, computational efficiency, interpretability, overfitting, sequential data analysis, AutoML, explainable AI

Abstract

The method of time series forecasting stands crucial in multiple application areas that include finance as well as healthcare and energy management and climate modeling. RNNs serve as a powerful tool under deep learning because they possess ability to detect sequential data patterns while extracting temporal dependencies from time series data using traditional statistical methods which were previously the dominant approach. This paper conducts an organized review of modern techniques for predicting time series data by using RNNs. This discussion covers three major RNN architectures together with Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) as well as their combination with hybrid models. The paper examines how RNN-based models perform against traditional approaches before addressing RNN-based forecasting problems and suggesting potential research paths for the future.

The analysis reviews multiple performance indicators utilized in past research to establish profound knowledge about RNN-based forecasting methods. The paper examines RNN benefits while analyzing the computational limitations and overfitting risks and interpretability problems that RNN systems encounter. The review investigates new frameworks including attention systems together with strengthening strategies and combination methods of statistical analysis with machine learning structures. Research outcomes demonstrate that RNN models particularly LSTM and GRU achieve great forecasting precision but future application research needs to optimize execution performance and advance interpretability capabilities of these models.

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References

Eugen Diaconescu. (2008). The use of NARX neural networks to predict chaotic time series. WSEAS Transactions on Computers Archive, 3(3), 182–191

Capizzi, G., Napoli, C., & Bonanno, F. (2012). Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting. IEEE Transactions on Neural Networks and Learning Systems, 23(11), 1805–1815. https://doi.org/10.1109/tnnls.2012.2216546

He, W. (2017). Load Forecasting via Deep Neural Networks. Procedia Computer Science, 122, 308–314. https://doi.org/10.1016/j.procs.2017.11.374

Freeman, B. S., Taylor, G., Gharabaghi, B., & Thé, J. (2018). Forecasting air quality time series using deep learning. Journal of the Air & Waste Management Association, 68(8), 866–886. https://doi.org/10.1080/10962247.2018.1459956

Bowes, B. D., Sadler, J. M., Morsy, M. M., Behl, M., & Goodall, J. L. (2019). Forecasting Groundwater Table in a Flood Prone Coastal City with Long Short-term Memory and Recurrent Neural Networks. Water, 11(5), 1098. https://doi.org/10.3390/w11051098

Hariadi, V., Saikhu, A., Zakiya, N., Wijaya, A. Y., & Baskoro, F. (2019). Multivariate Time Series Forecasting Using Recurrent Neural Networks for Meteorological Data. Conference SENATIK STT Adisutjipto Yogyakarta, 5. https://doi.org/10.28989/senatik.v5i0.365

Huang, S., Wang, D., Wu, X., & Tang, A. (2019). DSANet. Conference on Information and Knowledge Management. https://doi.org/10.1145/3357384.3358132

Jiang, X., & Adeli, H. (2005). Dynamic Wavelet Neural Network Model for Traffic Flow Forecasting. Journal of Transportation Engineering, 131(10), 771–779. https://doi.org/10.1061/(asce)0733-947x(2005)131:10(771)

Kumar, D., Singh, A., Samui, P., & Jha, R. K. (2019). Forecasting monthly precipitation using sequential modelling. Hydrological Sciences Journal, 64(6), 690–700. https://doi.org/10.1080/02626667.2019.1595624

Kumar, S., Hussain, L., Banarjee, S., & Reza, M. (2018). Energy Load Forecasting using Deep Learning Approach-LSTM and GRU in Spark Cluster. 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT). https://doi.org/10.1109/eait.2018.8470406

Lara-Benítez, P., Carranza-García, M., Luna-Romera, J. M., & Riquelme, J. C. (2020). Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting. Applied Sciences, 10(7), 2322. https://doi.org/10.3390/app10072322

Liang, Y., Ke, S., Zhang, J., Yi, X., & Zheng, Y. (2018). GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/476

Lin, T., Guo, T., & Aberer, K. (2017). Hybrid Neural Networks for Learning the Trend in Time Series. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2017/316

Lin, Y., Mago, N., Gao, Y., Li, Y., Chiang, Y.-Y., Shahabi, C., & Ambite, J. L. (2018). Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning. Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. https://doi.org/10.1145/3274895.3274907

Liu, Q., Wang, B., & Zhu, Y. (2018). Short-Term Traffic Speed Forecasting Based on Attention Convolutional Neural Network for Arterials. Computer-Aided Civil and Infrastructure Engineering, 33(11), 999–1016. https://doi.org/10.1111/mice.12417

Muzaffar, S., & Afshari, A. (2019). Short-Term Load Forecasts Using LSTM Networks. Energy Procedia, 158, 2922–2927. https://doi.org/10.1016/j.egypro.2019.01.952

Rahman, M. M., Shakeri, M., Tiong, S. K., Khatun, F., Amin, N., Pasupuleti, J., & Hasan, M. K. (2021). Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks. Sustainability, 13(4), 2393. https://doi.org/10.3390/su13042393

Rout, A. K., Dash, P. K., Dash, R., & Bisoi, R. (2017). Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach. Journal of King Saud University - Computer and Information Sciences, 29(4), 536–552. https://doi.org/10.1016/j.jksuci.2015.06.002

Syama Sundar Rangapuram, Seeger, M. W., Gasthaus, J., Stella, L., Wang, Y., & Januschowski, T. (2018). Deep State Space Models for Time Series Forecasting. Neural Information Processing Systems, 31, 7785–7794.

Torres, J. F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., & Troncoso, A. (2020). Deep Learning for Time Series Forecasting: A Survey. Big Data, 9(1). https://doi.org/10.1089/big.2020.0159

V, A., P, G., R, V., & K P, S. (2018). DeepAirNet: Applying Recurrent Networks for Air Quality Prediction. Procedia Computer Science, 132, 1394–1403. https://doi.org/10.1016/j.procs.2018.05.068

Vlachas, P. R., Byeon, W., Wan, Z. Y., Sapsis, T. P., & Koumoutsakos, P. (2018). Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 474(2213), 20170844. https://doi.org/10.1098/rspa.2017.0844