Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Partially observed Markov process (POMP) models, also known as hidden Markov models
or state space models, are ubiquitous tools for time series analysis. The R package pomp …

This new edition updates Durbin & Koopman's important text on the state space approach to
time series analysis. The distinguishing feature of state space time series models is that …

Objective: This paper aims to compare the performance of several commonly known
machine-learning (ML) models versus a classic Autoregression with Exogenous inputs …

In Borchers et al.(2002), a unifying framework was given for the plethora of methods for
estimating the abundance of closed populations of animals. In Chap. 13 of that book, the …

We propose a new method for decomposing seasonal data: a seasonal-trend
decomposition using regression (STR). Unlike other decomposition methods, STR allows for …

We propose new generic methods for decomposing seasonal data: STR (a Seasonal-Trend
decomposition procedure based on Regression) and Robust STR. In some ways, STR is …

Data collected for building a road safety observatory usually include observations made
sequentially through time. Examples of such data, called time series data, include annual (or …

We give an overview of some of the software tools available in R, either as built-in functions
or contributed packages, for the analysis of state space models. Several illustrative …

Time series forecasting plays an important role in many decision support systems, also in
those related to the management of supply chains. Forecast accuracy is, therefore, essential …