Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package
Source abstract
Abstract
Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Load and Power Forecasting · Stock Market Forecasting Methods · Meteorological Phenomena and Simulations
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Defence Institute of Advanced Technology, Pune 411025, India
Computer Science Department, Dijlah University College, Al-Dora, Baghdad 00964, Iraq
Artificial Intelligence Research Unit (AIRU), Dijlah University College, Al-Dora, Baghdad 00964, Iraq
Center for Quantitative Genetics and Genomics, Aarhus University, 8000 Aarhus, Denmark
Directorate of Engineering the Future, School of Science, Engineering and Environment, The University of Salford, Newton Building, Salford M5 4WT, UK
Department of Civil Engineering Science, School of Civil Engineering and the Built Environment, University of Johannesburg, Kingsway Campus, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa
Department of Town Planning, Engineering Networks and Systems, South Ural State University, 76, Lenin Prospekt, 454080 Chelyabinsk, Russia
Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Nasiriyah 64001, Iraq
Scientific Research Center, Al-Ayen University, Thi-Qar 64001, Iraq
Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
Adjunct Research Fellow, USQ’s Advanced Data Analytics Research Group, School of Mathematics Physics and Computing, University of Southern Queensland, Springfield, QLD 4350, Australia
New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar 64001, Iraq
Funding
WATERAGRI (European Union Horizon 2020 research and innovation program)
References (60)
Show source-provided references
1. A Novel Machine Learning based Computing Algorithm in Modeling of Soiled Photovoltaic Module
2. Bokde, N., Feijóo, A., Villanueva, D., and Kulat, K. (2019). A review on hybrid empirical mode decomposition models for wind speed and wind power prediction. Energies, 12.10.3390/en12020254 ↗
3. Analysis of differencing and decomposition preprocessing methods for wind speed prediction10.1016/j.asoc.2018.07.041 ↗
4. Financial time series forecasting model based on CEEMDAN and LSTM10.1016/j.physa.2018.11.061 ↗
5. Fast and Adaptive Cointegration Based Model for Forecasting High Frequency Financial Time Series10.1007/s10614-017-9691-7 ↗
6. Shih, H., and Rajendran, S. (2019). Comparison of time series methods and machine learning algorithms for forecasting Taiwan Blood Services Foundation’s blood supply. J. Healthc. Eng., 2019.10.1155/2019/6123745 ↗
7. Vázquez, M., Melin, P., and Prado-Arechiga, G. (2020). Hybrid Neural-Fuzzy Modeling and Classification System for Blood Pressure Level Affectation. Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine, Springer.10.1007/978-3-030-34135-0_18 ↗
8. Time Series Analysis and Forecasting of Oilseeds Production in India: Using Autoregressive Integrated Moving Average and Group Method of Data Handling–Neural Network
9. Paliwal, V., Ghare, A.D., Mirajkar, A.B., Bokde, N.D., and Feijoo Lorenzo, A.E. (2020). Computer Modeling for the Operation Optimization of Mula Reservoir, Upper Godavari Basin, India, Using the Jaya Algorithm. Sustainability, 12.10.3390/su12010084 ↗
10. Student Performance Predictions for Advanced Engineering Mathematics Course With New Multivariate Copula Models10.1109/ACCESS.2022.3168322 ↗
11. Acycle: Time-series analysis software for paleoclimate research and education10.1016/j.cageo.2019.02.011 ↗
12. A methodology for energy multivariate time series forecasting in smart buildings based on feature selection10.1016/j.enbuild.2019.05.021 ↗
13. Hybrid leakage management for water network using PSF algorithm and soft computing techniques10.1007/s11269-017-1859-3 ↗
14. A study on predictive model for forecasting anti-aircraft missile spare parts demand based on machine learning
15. A novel imputation methodology for time series based on pattern sequence forecasting10.1016/j.patrec.2018.09.020 ↗
16. Dew Point Time Series Forecasting at the North Dakota10.51526/kbes.2021.2.2.24-34 ↗
17. Newly explored machine learning model for river flow time series forecasting at Mary River, Australia10.1007/s10661-020-08724-1 ↗
18. A comparison between reconstruction methods for generation of synthetic time series applied to wind speed simulation10.1109/ACCESS.2019.2941826 ↗
19. 25 years of time series forecasting10.1016/j.ijforecast.2006.01.001 ↗
20. Time series forecasting using a hybrid ARIMA and neural network model10.1016/S0925-2312(01)00702-0 ↗
21. Hu, H., Zhang, J., and Li, T. (2020). A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series. Complexity, 2020.10.1155/2020/4064851 ↗
22. Energy time series forecasting based on pattern sequence similarity10.1109/TKDE.2010.227 ↗
23. Improved pattern sequence-based forecasting method for electricity load10.1002/tee.22024 ↗
24. Shen, W., Babushkin, V., Aung, Z., and Woon, W.L. (2013, January 21–24). An ensemble model for day-ahead electricity demand time series forecasting. Proceedings of the Fourth International Conference on Future Energy Systems, ACM, Berkeley, CA, USA.10.1145/2487166.2487173 ↗
25. Koprinska, I., Rana, M., Troncoso, A., and Martínez-Álvarez, F. (2013, January 4–9). Combining pattern sequence similarity with neural networks for forecasting electricity demand time series. Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA.10.1109/IJCNN.2013.6706838 ↗
26. Fujimoto, Y., and Hayashi, Y. (2012, January 11–14). Pattern sequence-based energy demand forecast using photovoltaic energy records. Proceedings of the 2012 International Conference on Renewable Energy Research and Applications (ICRERA), Nagasaki, Japan.10.1109/ICRERA.2012.6477299 ↗
27. PSF: Introduction to R Package for Pattern Sequence Based Forecasting Algorithm10.32614/RJ-2017-021 ↗
28. Bokde, N., Asencio-Cortés, G., and Martínez-Álvarez, F. (2017). PSF: Forecasting of Univariate Time Series Using the Pattern Sequence-Based Forecasting (PSF) Algorithm, R Package Version 0.4, R Foundation for Statistical Computing.
29. Bokde, N., Troncoso, A., Asencio-Cortés, G., Kulat, K., and Martínez-Álvarez, F. (2017, January 27–29). Pattern sequence similarity based techniques for wind speed forecasting. Proceedings of the International Work-Conference on Time Series (ITISE), Granada, Spain.
30. Bokde, N., Tranberg, B., and Andresen, G.B. (2020). A graphical approach to carbon-efficient spot market scheduling for Power-to-X applications. Energy Convers. Manag., 224.10.1016/j.enconman.2020.113461 ↗
31. Bokde, N.D., Tranberg, B., and Andresen, G.B. (2021). Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling. Appl. Energy, 281.10.1016/j.apenergy.2020.116061 ↗
32. Patil, J., Bokde, N., Mishra, S.K., and Kulat, K. (2020). PSF-Based Spectrum Occupancy Prediction in Cognitive Radio. Advanced Engineering Optimization Through Intelligent Techniques, Springer.10.1007/978-981-13-8196-6_53 ↗
33. Bokde, N., Feijóo, A., Al-Ansari, N., Tao, S., and Yaseen, Z.M. (2020). The hybridization of ensemble empirical mode decomposition with forecasting models: Application of short-term wind speed and power modeling. Energies, 13.10.3390/en13071666 ↗
34. Song, T., Ding, W., Liu, H., Wu, J., Zhou, H., and Chu, J. (2020). Uncertainty Quantification in Machine Learning Modeling for Multi-Step Time Series Forecasting: Example of Recurrent Neural Networks in Discharge Simulations. Water, 12.10.3390/w12030912 ↗
35. Niu, W.J., Feng, Z.K., Chen, Y.B., Zhang, H.R., and Cheng, C.T. (2020). Annual streamflow time series prediction using extreme learning machine based on gravitational search algorithm and variational mode decomposition. J. Hydrol. Eng., 25.10.1061/(ASCE)HE.1943-5584.0001902 ↗
36. Mazher, A. (2020). Visualization Framework for High-Dimensional Spatio-Temporal Hydrological Gridded Datasets using Machine-Learning Techniques. Water, 12.10.3390/w12020590 ↗
37. (2021, December 01). PSF_Py. Python Package Version 0.1. Available online: https://pypi.org/project/PSF-Py/.
38. (2019, December 31). GitHub PSF_Py. Available online: https://github.com/Mayur1009/PSF_py.
39. Hyndman, R.J., and Khandakar, Y. (2007). Automatic Time Series for Forecasting: The Forecast Package for R, Department of Econometrics and Business Statistics, Monash University. Number 6/07.
40. predtoolsTS: R package for streamlining time series forecasting10.1007/s13748-019-00193-z ↗
41. Bokde, N.D., Yaseen, Z.M., and Andersen, G.B. (2020). ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling. Energies, 13.10.3390/en13102578 ↗
42. cleanTS: Automated (AutoML) tool to clean univariate time series at microscales10.1016/j.neucom.2022.05.057 ↗
43. Performance of Hybrid Neuro-Fuzzy Model for Solar Radiation Simulation at Abuja, Nigeria: A Correlation Based Input Selection Technique
44. Yaseen, Z.M. (2021). An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions. Chemosphere, 277.10.1016/j.chemosphere.2021.130126 ↗
45. The Box-Jenkins approach to time series analysis10.1051/ro/1977110100031 ↗
46. Keeling, C.D., Whorf, T.P., and Scripps Institution of Oceanogra-phy (SIO) University of California, La Jolla (2021, December 01). Data 2000, Available online: http://cdiac.esd.ornl.gov/trends/co2/sio-mlo.htm.
47. Abd Rahman, N., Muhammad, N.S., Abdullah, J., and Wan Mohtar, W.H.M. (2019). Model performance indicator of aging pipes in a domestic water supply distribution network. Water, 11.10.3390/w11112378 ↗
48. The potential of novel data mining models for global solar radiation prediction10.1007/s13762-019-02344-0 ↗
49. Effectiveness of Electrical Capacitance Volume Tomography Method in Soil Water Content Measurement
50. Guo, L., Fu, P., Shi, T., Chen, Y., Zhang, H., Meng, R., and Wang, S. (2020). Mapping field-scale soil organic carbon with unmanned aircraft system-acquired time series multispectral images. Soil Tillage Res., 196.10.1016/j.still.2019.104477 ↗
51. Metaheuristic Optimization Algorithms Hybridized With Artificial Intelligence Model for Soil Temperature Prediction: Novel Model10.1109/ACCESS.2020.2979822 ↗
52. Machine learning for pore-water pressure time-series prediction: Application of recurrent neural networks10.1016/j.gsf.2020.04.011 ↗
53. Naganna, S.R., Deka, P.C., Ghorbani, M.A., Biazar, S.M., Al-Ansari, N., and Yaseen, Z.M. (2019). Dew point temperature estimation: Application of artificial intelligence model integrated with nature-inspired optimization algorithms. Water, 11.10.3390/w11040742 ↗
54. Nearing, G.S., Kratzert, F., Sampson, A.K., Pelissier, C.S., Klotz, D., Frame, J.M., Prieto, C., and Gupta, H.V. (2020). What role does hydrological science play in the age of machine learning?. Water Resour. Res., 57.10.31223/OSF.IO/3SX6G ↗
55. Azad, A., Kashi, H., Farzin, S., Singh, V.P., Kisi, O., Karami, H., and Sanikhani, H. (2020). Novel approaches for air temperature prediction: A comparison of four hybrid evolutionary fuzzy models. Meteorol. Appl., 27.10.1002/met.1817 ↗
56. An empirical estimation for time and memory algorithm complexities: Newly developed R package10.1007/s11042-020-09471-8 ↗
57. Agenis, M., and Bokde, N. (2019). GuessCompx: Empirically Estimates Algorithm Complexity, R Foundation for Statistical Computing. R Package Version 1.0.3.
58. Data mining-based upscaling approach for regional wind power forecasting: Regional statistical hybrid wind power forecast technique (RegionalSHWIP)10.1109/ACCESS.2019.2956203 ↗
59. How well do operational Numerical Weather Prediction configurations represent hydrology?10.1175/JHM-D-18-0086.1 ↗
60. Bokde, N., Feijóo, A., Villanueva, D., and Kulat, K. (2018). A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods. Energies, 11.10.3390/en11112923 ↗