Visibility Forecasting Using Autoregressive Integrated Moving Average (ARIMA) Models

Abstract Weather forecasting has gained researchers from worldwide societies over decades due to its substantial impact on global human life from agriculture, air traffic control to public health and safety. Although rigorous weather forecasting research has begun in 19th century, research has been dedicated to weather forecasting tasks that have significantly increased after weather-big data is widely available. This article proposes an Auto-regressive Integrated Moving Average (ARIMA) model to forecast better visibility for the variant value of parameters p, d, q using the grid technique. This experiment showed that ARIMA has the lowest MSE value of 0.00029 and a coefficient of variation value of 0.00315. The greater number of prediction data in the ARIMA model increases the MSE value.