Forecasting time series has acquired immense research importance and has vast
applications in the area of air pollution monitoring. This work attempts to investigate the …

Time series forecasting is the process of predicting future values of a time series from
knowledge of its past data. Although there are several models for making short-term …

The assessment of climate change, especially in terms of rainfall variability, is of giant
concern all over the world at present. Contemplating the high spatiotemporal variation in …

Time series analysis and prediction have attained significant attention from the research
community in the past few decades. However, the prediction accuracy of the models highly …

Understanding rainfall processes is crucial in addressing several hydrological challenges
that have both positive and negative impacts on agriculture, climate change, and natural …

Drought as an exigent natural phenomenon, with high frequency in arid and semi-arid
regions, leads to enormous damage to agriculture, economy, and environment. In this study …

Hydrological time series is affected by many factors and it is difficult to be forecasted
accurately by traditional forecast models. In this paper, a hydrological time series forecast …

Distributed wind-hybrid microgrids have the potential to provide key resilience and
economic benefits to both the customers they serve and the utility grids they are connected …

Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed
for monthly rainfall time series. Normality of the rainfall time series was achieved by using …

The prediction of time to slope failure (TOF) is one of the most pivotal concerns for both
geological risk researchers and practitioners. Conventional inverse velocity method (IVM) …