Time Series Prediction Based on Facebook Prophet: A Case Study, Temperature Forecasting in Myintkyina

Year 2020, , 263 - 267, 31.12.2020

Zar Zar Oo , Sabai Phyu

https://doi.org/10.18100/ijamec.816894

Cited By: 11

Abstract

Temperature forecasting is a progressive and time series analysis process to forecast the state of the temperature for a certain location in coming time. Nowadays, agriculture and manufacturing sectors are mostly dependent on temperature so forecasting is important to be precise because temperature warnings can save life and property. In this work, the Prophet Forecasting Model is used for Myitkyina's annual temperature forecasting using historical (2010 to 2017) time series data. Myitkyina is the capital city of the northernmost state (Kachin) in Myanmar, located 1480 kilometers from Yangon. Prophet is a modular regression model for time series predictions with high accuracy by using simple interpretable parameters that consider the effect of custom seasonality and holidays. In this study, the temperature forecasting model is proposed by using weather dataset provided by an International institution, National Oceanic and Atmospheric Administration (NOAA). This work implements the multi-step univariate time series prediction model and compares the forecasted value against the actual data. Such findings check that the proposed forecasting model provides an efficient and accurate prediction for temperature in Myitkyina.

Keywords

Facebook Prophet Model, Time series data, Time series prediction

Supporting Institution

University of Computer Studies, Yangon

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-fb-prophet-tutorial.
• [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.