Thermal Science 2023 Volume 27, Issue 4 Part B, Pages: 3081-3088
https://doi.org/10.2298/TSCI2304081T
Full text (
600 KB)
Cited by
Analysis and forecasting of temperature using time series forecasting methods a case study of Mus
Tugal Ihsan (Department of Software Engineering, Mus Alparslan University, Mus, Turkey), i.tugal@alparslan.edu.tr
Sevgin Fatih (Department of Construction Technology, Mus Alparslan University, Mus, Turkey)
The aim of this study is to forecast the daily average temperature of Mus
province in Turkey using time series methods. The performance of three time
series forecasting models is compared: LSTM, PROPHET, and ARIMA. The
behavior of these models in temperature data is also investigated. It is
found that these methods give accurate results according to the MAE, MSE,
and RMSE error metrics. However, LSTM produces slightly better results. The
temperature data used in this study was obtained from the Mus Meteorology
Provincial Directorate. Accurate temperature forecasting is important for
many different areas, from energy, agriculture to water resource management.
This study is an important research step in temperature analysis and
forecasting, and it will contribute to relevant decision-making processes.
Keywords: time series, prophet, LSTM, ARIMA, temperature forecasting
Show references
Sardans, J., et al., Warming And Drought Alter Soil Phosphatase Activity and Soil P Availability in a Mediterranean Shrubland, Plant Soil, 289 (2006), 1-2, pp. 227-238
Smith, B. A., et al., Improving Air Temperature Prediction With Artificial Neural Networks., Int. J. Comput. Intell., 3 (2006), 3, pp. 179-186
Malakouti, S. M., Utilizing Time Series Data From 1961 To 2019 Recorded Around the World and Machine Learning to Create a Global Temperature Change Prediction Model, Case Stud. Chem. Environ. Eng., 7 (2023), June, 100312
Tran, T. T. K., et al., Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series?, Atmosphere (Basel)., 11 (2020), 10, 1072
Abhishek, K., et al., Weather Forecasting Model Using Artificial Neural Network, Procedia Technol., 4 (2012), Dec., pp. 311-318
Qiu, R., et al., River Water Temperature Forecasting Using a Deep Learning Method, J. Hydrol., 595 (2021), Apr., 126016
Zhengxin, L., Yue, Z., Application of Fuzzy Control Based on Time Series Prediction Algorithm in Main Steam Temperature System, Proceedings, Chinese Automation Congress (CAC), Xi'an, China, 2018, Nov., pp. 116-121
Wei, K., Du, M., A Temperature Prediction Method of IGBT Based on Time Series Analysis, Proceedings, The 2nd International Conference on Computer and Automation Engineering (ICCAE), Singapore, 2010, pp. 154-157
Zhang, W. Y., et al., Single-Step and Multi-Step Time Series Prediction for Urban Temperature Based on LSTM Model of TensorFlow, Proceedings, 2021 Photonics & Electromagnetics Research Symposium (PIERS), Hangzhou, China, 2021, pp. 1531-1535
Hochreiter, S., Schmidhuber, J., Long Short-Term Memory, Neural Comput., 9 (1997), 8, pp. 1735-1780
Gers, F. A., Learning to Forget: Continual Prediction with LSTM, Proceedings, 9th International Conference on Artificial Neural Networks: ICANN ’99, Edinburg, UK, 1999, Vol. 1999, pp. 850-855
Wang, X., et al., LSTM-Based Broad Learning System For Remaining Useful Life Prediction, Mathematics, 10 (2022), 12, 2066
Kara, A., Global Solar Irradiance Time Series Estimation Using Long-Short-Term Memory Network, Gazi Univ. Nat. Sci. J. Part C Des. ve Technol., 7 (2019), 4, pp. 882-892
Hu, B., Research on Natural Language Processing Problems Based on LSTM Algorithm, Proceedings, 3rd Asia-Pacific Conference on Image Processing, Electronics and Computers, New York, USA, 2022, pp. 259-263
Tombaloğlu, B., Erdem, H., Turkish Speech Recognition Techniques and Applications of Recurrent Units (LSTM And GRU), Gazi Univ. J. Sci., 34 (2021), 4, pp. 1035-1049
Shibuya, E., Hotta, K., Cell Image Segmentation by Using Feedback and Convolutional LSTM, Vis. Comput., 38 (2022), 11, pp. 3791-3801
***, PROPHET, PROPHET Time Series Model, https://facebook.github.io/prophet/docs/quick_start.html
Taylor, S. J., Letham, B., Forecasting at Scale, Am. Stat., 72 (2018), 1, pp. 37-45
Ning, Y., et al., A Comparative Machine Learning Study for Time Series Oil Production Forecasting: ARIMA, LSTM, And PROPHET, Comput. Geosci., 164 (2022), July, 105126
ArunKumar, K. E., et al., Forecasting the Dynamics of Cumulative COVID-19 Cases (Confirmed, Recovered and Deaths) For Top-16 Countries Using Statistical Machine Learning Models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Averag, Appl. Soft Comput., 103 (2021), May, 107161
van der Meer, D., et al., Energy Management System with PV Power Forecast to Optimally Charge EVs At The Workplace, IEEE Trans. Ind. Informatics, 14 (2018), 1, pp. 311-320