zoo is an R package providing an S3 class with methods for indexed totally ordered observations, such as irregular time series. Its key design goals are independence of a particular index/time/date class and consistency with base R and the "ts" class for regular time series. This paper describes how these are achieved within zoo and provides several illustrations of the available methods for "zoo" objects which include plotting, merging and binding, several mathematical operations, extracting and replacing data and index, coercion and NA handling. A subclass "zooreg" embeds regular time series into the "zoo" framework and thus bridges the gap between regular and irregular time series classes in R. / Series: Research Report Series / Department of Statistics and Mathematics

Demand forecasting for sales is a widely researched topic that is essential for a business to prepare for market changes and increase profits. Existing research primarily focus on data that is more suitable for machine learning applications compared to the data accessible to companies lacking prior machine learning experience. This thesis performs demand forecasting on a known sales dataset and a dataset accessed directly from such a company, in the hopes of gaining insights that can help similar companies better utilize machine learning in their business model. LigthGBM, Linear Regression and Random Forest models are used along with several regression error metrics and plots to compare the performance of the two datasets. Both data sets are preprocessed into the same structure based on equivalent features found in each set. The company dataset is determined to be unfit for machine learning forecasting even after preprocessing measures and multiple possible reasons are established. The main contributors are a lack of observations per article and uniformity through the time series.