Forecasting Sunspot Number Time Series with Autoregressive Models
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Researchers in many ﬁelds share a deep interest in the sunspot activity of the Sun. This kind of solar activity has many consequences for human interests, and thus, it is important to study the Sun’s behavior and be able to predict future sunspot appearances. This task is a problem of time series forecasting. Some of the most popular methods used to forecast time series data are autoregressive models that predict future data points using a linear combination of previous values. Then, there are neural networks, a popular new tool for regression that can perform with outstanding results. These machine learning models have been shown to forecast time series successfully. In this thesis we use a variety of neural network architectures based on the classic AR models to predict future values of sunspot activity.