 Data mining has become increasingly important for analyzing large data sets over the past decade. It encompasses a wide range of techniques which can be applied to various problems such as time series forecasting. Traditional statistical methods have been shown to perform well in some cases but data mining techniques often outperform them. This paper reviews the most recent developments in time series forecasting with a focus on electricity price and demand markets. A compact mathematical formulation of the most commonly used techniques is provided, along with a review of the latest literature. This article was authored by Francisco Martinez-Alvarez, Alicia Troncoso, Walberto Asensio-Cortez, and others.