The its.analysis R Package – Modelling Short Time Series Data

Abstract

Modelling data over time requires a set of unique and bespoke processes in order to deal with problems induced by temporal dependency and autocorrelation. Methods are well developed and widely applied for working with such data when sample sizes are high, but the issue of how to fit models which are methodologically robust but not under-powered or over-fitted on short time series data remains vexing. This research proposes an interrupted time series analysis model solution to this problem, and uses a Type II Sum Squares ANCOVA Lagged Dependent Variable, variance-centric approach as part of a newly introduced R package - its.analysis. Using this model switches the null hypothesis situation to a much more reliable test and allows for a much more flexible approach to adding covariates in small sample conditions. The model performs very well under test conditions, appears more conservative than existing alternative techniques, and as such is recommended to researchers for future analysis of temporal data where observations are limited (between 15 and 45 observations).

Keywords: time series, anova, interrupted time series, small data, methodology