Which machine learning algorithms can be used for time series forecasts?

Currently I am playing around with time series forecasts (specifically for Forex). I have seen some scientific papers about echo state networks which are applied to Forex forecast. Are there other good machine learning algorithms for this purpose?

It would also be interesting to extract "profitable" patterns from the time series.

Here are three survey papers that examine the use of machine learning in time series forecasting:

• "An Empirical Comparison of Machine Learning Models for Time Series Forecasting" by Ahmed, Atiya, El Gayar, and El-shishiny provides an empirical comparison of several machine learning algorithms, including:

"...multilayer perceptron, Bayesian neural networks, radial basis
functions, generalized regression neural networks (also called kernel
regression), K-nearest neighbor regression, CART regression trees,
support vector regression, and Gaussian processes."

• "Financial time series forecasting with machine learning techniques: A survey" by Krollner, Vanstone, and Finnie finds:

"...that artificial neural networks (ANNs) are the dominant machine
learning technique in this area."

• "Machine Learning Strategies for Time Series Forecasting" by Bontempi, Ben Taieb, and Le Borgne focuses on three aspects:

"...the formalization of one-step forecasting problems as supervised
learning tasks, the discussion of local learning techniques as an
effective tool for dealing with temporal data, and the role of the
forecasting strategy when we move from one-step to multiple-step
forecasting."

StackExchange Computer Science Q#13937, answer score: 28