Browse > Article
http://dx.doi.org/10.9709/JKSS.2021.30.4.001

Time Series Data Analysis using WaveNet and Walk Forward Validation  

Yoon, Hyoup-Sang (Dept. of Software Convergence, Daegu Catholic University)
Publication Information
Journal of the Korea Society for Simulation / v.30, no.4, 2021 , pp. 1-8 More about this Journal
Abstract
Deep learning is one of the most widely accepted methods for the forecasting of time series data which have the complexity and non-linear behavior. In this paper, we investigate the modification of a state-of-art WaveNet deep learning architecture and walk forward validation (WFV) in order to forecast electric power consumption data 24-hour-ahead. WaveNet originally designed for raw audio uses 1D dilated causal convolution for long-term information. First of all, we propose a modified version of WaveNet which activates real numbers instead of coded integers. Second, this paper provides with the training process with tuning of major hyper-parameters (i.e., input length, batch size, number of WaveNet blocks, dilation rates, and learning rate scheduler). Finally, performance evaluation results show that the prediction methodology based on WFV performs better than on the traditional holdout validation.
Keywords
time series forecasting; deep learning; WaveNet; work forward validation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Bergstra, J., Bardenet, R., Bengio, Y., and Kegl, B., "Algorithms for Hyper-parameter Otimization", Proc. of 25th Annual Conference on Neural Information Processing Systems, pp. 1-9, 2011
2 Cho K, Merrienboer B. van, Gulcehre C, Bahdanau, D., Bougares, F,. Schwenk, H., and Bengio, Y. "Learning Prase Rpresentations sing RNN Encoder-Decoder for Statistical Machine Translation", Proc. of Empirical Methods in Natural Language Processing. pp. 1724-1734. 2014.
3 Hochreiter, S. and Schmidhuber, J., "Long Short-Term Memory", Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997   DOI
4 Hong, T., Wang, P., and Willis, H. L., "A Naive Multiple Linear Regression Benchmark for Short Term Load Forecasting", Proc. of the IEEE Power and Energy Society General Meeting, vol. 2, pp. 1-6, 2011.
5 Karim, A. and Ariffin, S., "Electricity Load Forecasting in UTP Using Moving Averages and Exponential Smoothing Techniques", Applied Mathematical Sciences, vol. 7, no. 77-80, pp. 4003-4014, 2013.   DOI
6 Masters, D. and Luschi, C., "Revisiting Small Batch Training for Deep Neural Networks", 1-18. http://arxiv.org/abs/1804.07612, 2018
7 Ngoc T. T., van Dai, L., and Phuc D. T., "Grid Search of Multilayer Perceptron Based on the Walk-Forward Validation methodology", International Journal of Electrical and Computer Engineering, vol. 11, no, 2, pp. 1742-1751, 2021.
8 Livieris, I. E., Emmanuel, P., and Panagiotis, P., "A CNN-LSTM Model for Gold Price TimeSeries Forecasting", Neural Computing and Applications. vol. 32, no. 23, pp. 17351-17360, 2020.   DOI
9 Clements M. P., Franses P. H., and Swanson N. R., "Forecasting Economic and Financial Time-Series with Non-linear Models". International Journal of Forecast, vol. 20, no. 2, pp. 169-183, 2004.   DOI
10 Hong, T., Gui, M., Baran, M., and Willis, H. L., "Modeling and Forecasting Hourly Electric Load by Multiple Linear Regression with Interactions", Proc. of the IEEE Power and Energy Society General Meeting, pp. 1-8, 2010.
11 Oord, A. van den, Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K., "WaveNet: A Generative Model for Raw Audio", http://arxiv.org/abs/1609.03499, 2016.
12 Tealab, A., "Time Series Forecasting Using Artificial Neural Networks Methodologies: A Systematic Review." Future Computing and Informatics Journal, vol. 3, no. 2, pp. 334-340. 2018.   DOI