Holt’s exponential smoothing and neural network models for forecasting interval‐valued time series

Interval‐valued time series are interval‐valued data that are collected in a chronological sequence over time. This paper introduces three approaches to forecasting interval‐valued time series. The first two approaches are based on multilayer perceptron (MLP) neural networks and Holt’s exponential smoothing methods, respectively. In Holt’s method for interval‐valued time series, the smoothing parameters are estimated by using techniques for non‐linear optimization problems with bound constraints. The third approach is based on a hybrid methodology that combines the MLP and Holt models. The practicality of the methods is demonstrated through simulation studies and applications using real interval‐valued stock market time series.