  In the era of space exploration, coronal holes on the sun play a significant
role due to their impact on satellites and aircraft through their open magnetic
fields and increased solar wind emissions. This study employs computer vision
techniques to detect coronal hole regions and estimate their sizes using
imagery from the Solar Dynamics Observatory (SDO). Additionally, we utilize
hybrid time series prediction model, specifically combination of Long
Short-Term Memory (LSTM) networks and ARIMA, to analyze trends in the area of
coronal holes and predict their areas across various solar regions over a span
of seven days. By examining time series data, we aim to identify patterns in
coronal hole behavior and understand their potential effects on space weather.
