Forecast modeling of peak work loads based on time series analysis
The article deals with the analysis of work load in a technical documentation department of research-and-production association. Data for the analysis are formalized as unstructured time series (time series). Basic definitions of time series, methods for detecting anomalous data, trend criteria, trend equation types are studied. Methodology for data structuring of initial time series intended for the following analysis is developed. Modification of the criterion for anomalous point detection is proposed. All the considered algorithms are implemented; database structure is developed. Information on the department work performed during the period 2008 to 2018 was captured to carry out computing experiments. Data are structured according to the proposed methodology. Anomalous data are detected by standard and modified criteria; trend availability is identified. Conclusions are made on criteria and equations that are optimal for initial data set. Data for year 2018 are modelled based on various equations; results are compared with actual data. Conclusions were drawn up regarding the ability to be used the methods examined for the selected data set.