Abstract
A time-series is a collection of data points recorded at successive points in time and are a common occurrence in a diverse range of applications such as finance, energy, signal processing, astronomy, resource management and economics. Time-series prediction attempts to predict future events/behaviour based on historical data. In this endeavour, it is a considerable challenge to capture inherent nonlinear and non-stationary characteristics present in real-world data. Success, or otherwise, is strongly dependent on a suitable choice of input features which need to be extracted in an effective manner. Therefore, feature selection plays an important role in machine learning tasks.
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De Silva, A.M., Leong, P.H.W. (2015). Introduction. In: Grammar-Based Feature Generation for Time-Series Prediction. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-287-411-5_1
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