Global fire activity has a huge impact on human lives. In recent years, many
fire models have been developed to forecast fire activity. They present good
results for some regions but require complex parametrizations and input
variables that are not easily obtained or estimated. In this paper, we evaluate
the possibility of using historical data from 2003 to 2017 of active fire
detections (NASA's MODIS MCD14ML C6) and time series forecasting methods to
estimate global fire season severity (FSS), here defined as the accumulated
fire detections in a season. We used a hexagonal grid to divide the globe, and
we extracted time series of daily fire counts from each cell. We propose a
straightforward method to estimate the fire season lengths. Our results show
that in 99% of the cells, the fire seasons have lengths shorter than seven
months. Given this result, we extracted the fire seasons defined as time
windows of seven months centered in the months with the highest fire
occurrence. We define fire season severity (FSS) as the accumulated fire
detections in a season. A trend analysis suggests a global decrease in length
and severity. Since FSS time series are concise, we used the
monthly-accumulated fire counts (MA-FC) to train and test the seven forecasting
models. Results show low forecasting errors in some areas. Therefore we
conclude that many regions present predictable variations in the FSS
