Confidence Interval Approach to Weather Forecasting with Horizon Based Genetic Programming
Abstract
Being able to forecast events has always been important for humans. Humans did forecasting by inspecting movements of material and non-material objects in ancient times. However, thanks to the technological developments and the increasing amount of data in recent years, forecasting is now done by computers, especially by machine learning methods. One of the areas where these methods are used frequently is numerical weather forecasting. In this type of forecast, short, medium and long-term weather forecasts are made using historical data. However, predictions are inherently error-prone phenomena and should be stated which error range the predictions fall. In this study, numerical weather forecasting was done by combining Genetic Programming and Inductive Conformal Prediction method. The effect of 10 and 20 days of historical data on short (1-day), medium (3-days) and long-term (5-days) weather forecasts was examined. Results suggested that Genetic Programming has a good potential to be used in this area. However, when Genetic Programming was combined with the Inductive Conformal Prediction method, it was shown that forecasts gave meaningful results only in short-term; forecasts made for medium and long-term did not produce meaningful results.
Keywords
Artificial Intelligence, Confidence Interval, Prediction, Genetic Programming
References
- Ning, Y., Kazemi, H., & Tahmasebi, P. (2022). A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet. Computers & Geosciences, 164, 105126. Doi: 10.1016/j.cageo.2022.105126
- Sean J. Taylor & Benjamin Letham (2018) Forecasting at Scale, The American Statistician, 72:1, 37-45, Doi: 10.1080/00031305.2017.1380080
- W. Kong, Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, and Y. Zhang, “Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network,” IEEE Transactions on Smart Grid, vol. 10, no. 1, pp. 841–851, Jan. 2019, Doi: 10.1109/TSG.2017.2753802.
- X. Xiao, H. Mo, Y. Zhang, and G. Shan, “Meta-ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting,” Energy, vol. 246, p. 123418, May 2022, Doi: 10.1016/j.energy.2022.123418.
- S. R. Ghaffari-Razin, A. R. Moradi, and N. Hooshangi, “Modeling and forecasting of ionosphere TEC using least squares SVM in central Europe,” Advances in Space Research, Jun. 2022, Doi: 10.1016/j.asr.2022.06.020.
- Fan Li & Guang Jin (2022) Research on power energy load forecasting method based on KNN, International Journal of Ambient Energy, 43:1, 946-951, Doi: 10.1080/01430750.2019.1682041
- M. Lehna, F. Scheller, and H. Herwartz, “Forecasting day-ahead electricity prices: A comparison of time series and neural network models taking external regressors into account,” Energy Economics, vol. 106, p. 105742, Feb. 2022, Doi: 10.1016/j.eneco.2021.105742.
- A. Brusaferri, M. Matteucci, P. Portolani, and A. Vitali, “Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices,” Applied Energy, vol. 250, pp. 1158–1175, 2019, Doi: 10.1016/j.apenergy.2019.05.068
- X. Gong, W. Zhang, W. Xu, and Z. Li, “Uncertainty index and stock volatility prediction: evidence from international markets,” Financial Innovation, vol. 8, no. 1, p. 57, Jun. 2022, Doi: 10.1186/s40854-022-00361-6.
- F. Baart, M. van Ormondt, J. van T. de Vries, and M. van Koningsveld, “Morphological impact of a storm can be predicted three days ahead,” Computers & Geosciences, vol. 90, pp. 17–23, 2016, Doi: 10.1016/j.cageo.2015.11.011.