Research

Quantitative Consulting has many interesting research publications. Some of them were completed for the company purposes and others were outcomes of the academic research of our employees. Some of the content is accessible only after registration of your email address, rest of the content is downloadable from this site.

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Methods for periodic and irregular time series, Tomáš Hanzák, 2014

Methods for periodic and irregular time series, Tomáš Hanzák, 2014

Discounted least squares, Exponential smoothing, Holt-Winters method, irregular observations, time series periodicity

The thesis primarily deals with modifications of exponential
smoothing type methods for univariate time series with periodicity and/or certain types of irregularities. A modified Holt method for irregular times series robust to the problem of time-close observations is suggested. The general concept of seasonality modeling is introduced into Holt-Winters method including a linear interpolation of seasonal indices and usage of trigonometric functions
as special cases (the both methods are applicable for irregular observations). The DLS estimation of linear trend with seasonal dummies is investigated and compared with the additive Holt-Winters method. An autocorrelated term is introduced as an additional component in the time series decomposition. The suggested methods are compared with the classical ones using real data examples and/or simulation studies.

31.03.2014

Holt-Winters method with general seasonality, Tomáš Hanzák, 2012

Holt-Winters method with general seasonality, Tomáš Hanzák, 2012

Exponential smoothing, Holt–Winters method, irregular time series, seasonal indices, trigonometric functions

The paper suggests a generalization of widely used Holt–Winters smoothing and forecasting method for seasonal time series. The general concept of seasonality modeling is introduced both for the additive and multiplicative case. Several special cases are discussed, including a linear interpolation of seasonal indices and a usage of trigonometric functions. Both methods are fully applicable for time series with irregularly observed data (just the special case of missing observations was covered up to now). Moreover, they sometimes outperform the classical Holt–Winters method even for regular time series. A simulation study and real data examples compare the suggested methods with the classical one.

01.01.2012

Exponential Smoothing for Irregular Time Series, Tomáš Hanzák, 2008 (in Czech)

Exponential Smoothing for Irregular Time Series, Tomáš Hanzák, 2008 (in Czech)

Exponential smoothing, irregular times series

Exponential Smoothing for Irregular Time Series. Poster for Robust 2008 conference. In this poster are highlighted problems resulting from irregularity of observations and suggested possible solutions. Beside already published methods are presented methods or their modifications by author ( m ranked exponential smoothing, irregularly observed ARIMA(0, 1, 1) process, modified Holt’s method, Holt-Winters method modeling seasonality by goniometric functions).

08.09.2008