<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The main aim of the article is to propose a forecasting procedure that could be useful in the case of randomly distributed zero-inflated time series. Many economic time series are randomly distributed, so it is not possible to estimate any kind of statistical or econometric models such as, for example, count data regression models. This is why in the article a new forecasting procedure based on the stochastic simulation is proposed. Before it is used, the randomness of the times series should be considered. The hypothesis stating the randomness of the times series with regard to both sales sequences or sales levels is verified. Moreover, in the article the ex post forecast error that could be computed also for a zero-inflated time series is proposed. All of the above mentioned parts were invented by the author. In the empirical example, the described procedure was applied to forecast the sales of products in a company located in the vicinity of Szczecin (Poland), so real data were analysed. The accuracy of the forecast was verified as well.</p></abstract>

The main aim of the article is to propose a forecasting procedure that could be useful in the case of randomly distributed zero-inflated time series. Many economic time series are randomly distributed, so it is not possible to estimate any kind of statistical or econometric models such as, for example, count data regression models. This is why in the article a new forecasting procedure based on the stochastic simulation is proposed. Before it is used, the randomness of the times series should be considered. The hypothesis stating the randomness of the times series with regard to both sales sequences or sales levels is verified. Moreover, in the article the ex post forecast error that could be computed also for a zero-inflated time series is proposed. All of the above mentioned parts were invented by the author. In the empirical example, the described procedure was applied to forecast the sales of products in a company located in the vicinity of Szczecin (Poland), so real data were analysed. The accuracy of the forecast was verified as well.

Forecasting Randomly Distributed Zero-Inflated Time Series

Folia Oeconomica Stetinensia

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License.

{"article-title":"Forecasting Randomly Distributed Zero-Inflated Time Series"}

The main aim of the article is to propose a forecasting procedure that could be useful in the case of randomly distributed zero-inflated time series. Many...

Forecasting Randomly Distributed Zero-Inflated Time Series

Published Online: Jun 16, 2017

Page range: 7 - 19

Received: Nov 25, 2016

Accepted: Apr 13, 2017

DOI: https://doi.org/10.1515/foli-2017-0001

Keywords
forecasting zero-inflated time series, count data models, sales forecasting system, stochastic simulation, Bernoulli processes

© University of Szczecin

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License.

Forecasting Randomly Distributed Zero-Inflated Time Series

Published Online: Jun 16, 2017

Page range: 7 - 19

Received: Nov 25, 2016

Accepted: Apr 13, 2017

DOI: https://doi.org/10.1515/foli-2017-0001

Keywordsforecasting zero-inflated time series, count data models, sales forecasting system, stochastic simulation, Bernoulli processes

© University of Szczecin

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License.

Keywords
forecasting zero-inflated time series, count data models, sales forecasting system, stochastic simulation, Bernoulli processes