Forecasting Financial Time Big Data
using Interval Time Series

An interval time series (ITS) assigns to each period an interval covering the values
taken by the variable. Each interval has four characteristic attributes, since it can be de ned
in terms of lower and upper boundaries, center and radius. The analysis and forecasting of ITS
is a very young research area, dating back less than 15 years, and still presents a wide array of
open issues. One main issue with time big data consists of deciding if to handle them as classic
time series (CTS) or to proceed with some kind of aggregation in order to get a time series of
symbolic data like ITS. In this paper both approaches using the k-Nearest Neighbours (kNN)
method are applied to forecast exchange rates. The reduction in error using ITS instead of CTS
suggests that the ITS approach could be a better way to forecast exchange rates using Big Data.
Some interesting conclusions about di erent frequency aggregation horizons are obtained and
further research issues are proposed.