AUTOREGRESSIVE TIME SERIES MODELS USED FOR FORECASTING DAILY MAXIMUM AND MINIMUM TEMPRATURE
Sukhpal Kaur Dr. Madhuchanda Rakshit · Zenodo (CERN European Organization for Nuclear Research) · 2019
Time series analysis and forecasting has become a major tool for analyzing, forecasting and in other applications in various meteorological and hydro logical data like temperature, rainfall, humidity, river flow, stream flow and many more others. In this paper our goal is to forecast the future trend of maximum and minimum temperature of Punjab, using non-seasonal and seasonal Autoregressive moving average and Seasonal autoregressive integrated moving average models. For this we collect and analyze the past values and developed appropriate models which describe the inherent structure and characteristics of the series. We calculated the accuracy of our results on the basis of error measures and residual normality test . The forecast results obtained by us provide a detailed explanation of model selection and forecasting accuracy is presented. Autoregressive time series models are widely used in forecasting and analyzing of various data that are also applicable in different research areas as our forecasting results can also be used by various agencies for planning and development of various meteorological and hydro logical decisions.