PREDICTING SOLAR FLARE OCCURRENCES USING TIME-SERIES DATA FROM NASA’S API FOR SPACE WEATHER FORECASTING
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Date
2024
Authors
Seit, Damir
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Nazarbayev University School of Engineering and Digital Sciences
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.
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Type of access: Restricted
Citation
Seit, D. (2024). Predicting Solar Flare Occurrences using Time-Series Data from NASA’s API for Space Weather Forecasting. Nazarbayev University School of Engineering and Digital Sciences