PREDICTING SOLAR FLARE OCCURRENCES USING TIME-SERIES DATA FROM NASA’S API FOR SPACE WEATHER FORECASTING

Abstract

This thesis investigates the complicated area of solar flare and Coronal Mass Ejection (CME) prediction using data science approaches and NASA’s Application Programming Interface (API) for the purpose of comparing time-series prediction models in terms of accuracy and interpretability. Quick, intense solar eruptions labeled as CMEs and solar flares have significant impacts on space weather and Earth’s technological systems. The data for this research is taken from the open NASA API service. To understand the underlying relationships governing these solar events, we utilize both state-of-the-art LSTM and well-known models such as Autoregressive Integrated Moving Average (ARIMA), Linear Regression, and Autoregressive (AR) model. The dataset was gathered using a specifically developed Python application with the help of RESTful API. A key part of our research is the dual assessment of these models, which evaluates in terms of both forecast accuracy and interpretability. The interpretability of the tested models is measured using the SOC (simulatability operations count) score metric, which counts the number of arithmetic operations performed to predict a single test case. This study shows that simpler approaches in univariate time-series forecasting perform better than more complex ones in terms of accuracy and interpretability with small dataset. However, only Linear Regression made a reasonable predictions of extreme peak events.