Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Modern studies from a variety of fields record multiple functional observations according to
either multivariate, longitudinal, spatial, or time series designs. We refer to such data as …

Introduction to Functional Data Analysis provides a concise textbook introduction to the field.
It explains how to analyze functional data, both at exploratory and inferential levels. It also …

A suitable feature representation that can both preserve the data intrinsic information and
reduce data complexity and dimensionality is key to the performance of machine learning …

Function and shape data analysis are old topics in statistics, studied off and on over the last
several decades. However, the early years of the new millennium saw a renewed focus and …

With the advance of modern technology, more and more data are being recorded
continuously during a time interval or intermittently at several discrete time points. These are …

This paper gives an account of some of the recent work on structural breaks in time series
models. In particular, we show how procedures based on the popular cumulative sum …

The theory and practice of statistical methods in situations where the available data are
functions (instead of real numbers or vectors) is often referred to as Functional Data Analysis …

From sparse to dense functional data and beyond Page 1 The Annals of Statistics 2016, Vol.
44, No. 5, 2281–2321 DOI: 10.1214/16-AOS1446 © Institute of Mathematical Statistics, 2016 …

In recent years, efficient modeling and forecasting of electricity prices became highly
important for all the market participants for develo** bidding strategies and making …