Time Series Prediction Grounded on Neural Prophet- Temperature Forecasting

Author
D.B.Shanmugam, P.M.Kavitha, M.Pazhanivelrajan, S.Prithiv Ganth, Dilli Babu
Keywords
National Oceanic and Atmospheric Administration; Forecasting Model; Time Series; Myitkyina; Neural Prophet Model
Abstract
Temperature determining is a moderate and time series investigation cycle to estimate the condition of the temperature for a specific area in coming time. These days, agribusiness and assembling areas are for the most part reliant upon temperature so determining is essential to be exact in light of the fact that temperature admonitions can save life and property. In this work, the Prophet Forecasting Model is utilized for Myitkyina’s yearly temperature estimating utilizing authentic (2010 to 2017) time series information. Myitkyina is the capital city of the northernmost state (Kachin) in Myanmar, found 1480 kilometres from Yangon. Prophet is a particular relapse model for time series forecasts with high precision by utilizing basic interpretable boundaries that think about the impact of custom irregularity and occasions. In this review, the temperature estimating model is proposed by utilizing climate dataset given by an International foundation, National Oceanic and Atmospheric Administration (NOAA). This work executes the multi-step univariate time series expectation model and analyses the anticipated worth against the real information. Such discoveries check that the proposed anticipating model gives an effective and exact expectation for temperature in Myitkyina.
References
[1] R. Adhikari and R. K. Agrawal, “An Introductory Study on Time Series Modeling and Forecasting”, M. Tech. thesis, Jawaharlal Nehru University, New Delhi, India, 2013.
[2] Sean J. Taylor and Benjamin Letham, “Forecasting at Scale”, September 2017.
[3] https:// www. kaggle. com/ armamut/ predicting- transactions-fbprophet-tutorial, Retrieved on 14 Feb 2022.
[4] Shaminder Singh, Pankaj Bhambri and Jasmeen Gill, “Time Series based Temperature Prediction usring Back Propagation with Genetic Algorithm Technique”, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 3, September 2011, pp. 28-32.
[5] Dr. S. Santhosh Baboo and I.Kadar Shereef, “An Efficient Weather Forecasting System using Artificial Neural Network”, International Journal of Environmental Science and Development, Vol. 1, No. 4, October 2010, pp. 321-326.
[6] Kuldeep Goswami and Arnab N. Patowary, “Monthly Temperature Prediction Based On ARIMA Model: A Case Study In Dibrugarh Station Of Assam, India”, International Journal of Advanced Research in Computer Science Volume 8, No. 8, September-October 2017, pp.292-298.
[7] Y. Liming, Y. Guixia and E. V. Ranst, “Time-Series Modeling and Prediction of Global Monthly Absolute Temperature for Environmental Decision Making”, Advances in Atmospheric Sciences, Volume. 30, No. 2, 2013, pp.382– 396.
[8] Y.Radhika and M.Shashi, Atmospheric Temperature Prediction using Support Vector Machines, International Journal of Computer Theory and Engineering, Vol. 1, No. 1, April 2009, pp. 55-58.

Received : 17 February 2022
Accepted : 22 March 2023
Published : 31 March 2023
DOI: 10.30726/esij/v10.i1.2023.101003

Time-Series-Prediction.pdf