It is becoming increasingly common for organizations to collect very large amounts of data
over time, and to need to detect unusual or anomalous time series. For example, Yahoo has
banks of mail servers that are monitored over time. Many measurements on server …

It is common practice to evaluate the strength of forecasting methods using collections of
well-studied time series datasets, such as the M3 data. The question is, though, how diverse
and challenging are these time series, and do they enable us to study the unique strengths …

How do people recognize and learn about complex functional structure? Taking inspiration
from other areas of cognitive science, we propose that this is achieved by harnessing
compositionality: complex structure is decomposed into simpler building blocks. We …

A crucial task in time series forecasting is the identification of the most suitable forecasting
method. We present a general framework for forecast-model selection using meta-learning.
A random forest is used to identify the best forecasting method using only time series …

Blind source separation is a basic topic in signal and image processing. Independent
component analysis is a basic solution to blind source separation. This chapter introduces
blind source separation, with importance attached to independent component analysis …

How do we perceive the predictability of functions? We derive a rational measure of a
function's predictability based on Gaussian process learning curves. Using this measure, we
show that the smoothness of a function can be more important to predictability judgments …

We propose a Bayesian nonparametric mixture model for the reconstruction and prediction
from observed time series data, of discretized stochastic dynamical systems, based on
Markov Chain Monte Carlo methods. Our results can be used by researchers in physical …

Every organism in an environment, whether biological, robotic or virtual, must be able to
predict certain aspects of its environment in order to survive or perform whatever task is
intended. It needs a model that is capable of estimating the consequences of possible …

We propose graph-based predictable feature analysis (GPFA), a new method for
unsupervised learning of predictable features from high-dimensional time series, where high
predictability is understood very generically as low variance in the distribution of the next …

The earth's ice–ocean–atmosphere system is made up of subsystems which have different
dynamics and which evolve at different timescales. Examples include the slow dynamics of
ice sheet growth and melting, the tropical response to precessional cycles (∼ 21,000 years) …