Time Series Prediction Based on Facebook Prophet: A Case Study, Temperature Forecasting in Myintkyina
Zar Zar Oo, Sabai Phyu
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.