Smoothing for discrete-valued time series

Smoothing Estimation for Discrete-Valued Time Series

We deal with the smoothed estimators for conditional probability functions of discrete-valued time series fY t g under two diierent settings. When the conditional distribution of Y t given its lagged values falls in a parametric family and depends on exogenous random variables, a smoothed maximum (partial) likelihood estimator for the unknown parameter is proposed. While there is no prior infor...

Smoothing Methods for Histogram-valued Time Series

We adapt smoothing methods to histogram-valued time series (HTS) by introducing a barycentric histogram that emulates the "average" operation, which is the key to any smoothing filter. We show that, due to its linear properties, only the Mallows-barycenter is acceptable if we wish to preserve the essence of any smoothing mechanism. We implement a barycentric exponential smoothing to forecast th...

Holt’s exponential smoothing model for interval-valued time series

Interval-valued time series are interval-valued data that are collected in a chronological sequence through time. This paper adapts an approach to forecasting interval valued-time series based on Holt’s exponential smoothing method. In the adapted Holt’s method for interval-valued time series, the smoothing parameters are estimated by using techniques for non-linear optimization problems with b...

Clustering life trajectories: A new divisive hierarchical clustering algorithm for discrete-valued discrete time series

A new adaptive exponential smoothing method for non-stationary time series with level shifts

Simple exponential smoothing (SES) methods are the most commonly used methods in forecasting and time series analysis. However, they are generally insensitive to non-stationary structural events such as level shifts, ramp shifts, and spikes or impulses. Similar to that of outliers in stationary time series, these non-stationary events will lead to increased level of errors in the forecasting pr...