GAMs for Customer Lifetime Value (CLV) prediction

Customer Lifetime Value models are critical for SaaS businesses, but standard regression approaches often predict impossible values like negative revenue or infinite growth. There are established methods to handle this (i.e. constrained optimization, truncation), but these can be complex to implement and maintain in production. Even fewer approaches naturally incorporate business logic while remaining interpretable and deployable. This post demonstrates how to build CLV models that automatically respect business reality using GAMs, creating predictions that make sense without complex constraint matrices or manual bounds checking.

Why you should embrace ecological forecasting

Ecological forecasting offers a powerful shift in how we approach research - moving from static, retrospective analyses to dynamic, iterative workflows that evolve with new data. This presentation challenges ecologists to rethink how they structure their projects by highlighting how forecasting forces us to confront uncertainty, clarify assumptions, and make our understanding of ecological systems explicit. By continuously testing predictions against observations, forecasting reveals both the strengths and limits of our models, encouraging more transparent and adaptive science. Two applied examples will illustrate how these ideas can be put into practice, offering inspiration for ecologists seeking to build more responsive and insightful research programmes.

Automatic forecasting for non-Gaussian time series

Automatic forecasting algorithms are crucial tools for many modern-day business operations. Yet nearly all of the available algorithms, and their associated software packages, assume that the series can be modelled with Gaussian errors. This makes it hard for end-users to employ these models when they routinely deal with non-Gaussian series, such as counts of sales, compositional data that must sum to 1, or time series that present as proportions. This project will develop extremely efficient and adaptable forecasting algorithms that can work with a huge variety of real-world time series, filling a crucial gap in the available software landscape for industry-level forecasting purposes.

The student will be based at The University of Queensland within the School of Veterinary Science, primarily supervised by Dr. Nicholas Clark. The candidate will work with a diverse group of international researchers. Additional support will be given by partners at the Ecological Forecasting Initiative and the Spatial Epidemiology Laboratory, including assistance in computer-based data analysis, model building and scientific communication. The selected student will have the opportunity to work with all partners on this project but will be based at UQ.

This project will help develop the candidate’s skills in critical thinking, project management, data management and analysis, writing and communication. Expected applications of the project are incredibly diverse, meaning the student will be well prepared for a future career in research or with government and non-government land management, biosecurity or conservation agencies.

Ecological forecasting with R 📦’s {mvgam} and {brms}

Time series analysis and forecasting are standard goals in applied ecology. But most time series courses focus only on traditional forecasting models such as ARIMA or Exponential Smoothing. These models cannot handle features that dominate ecological data, including overdispersion, clustering, missingness, discreteness and nonlinear effects. Using the flexible and powerful Bayesian modelling software Stan, we can now meet this complexity head on. R packages such as {mvgam} and {brms} can build Stan code to specify ecologically appropriate models that include nonlinear effects, random effects and dynamic processes, all with simple interfaces that are familiar to most R users. In this course you will learn how to wrangle, visualize and explore ecological time series. You will also learn to use the {mvgam} and {brms} packages to analyse a diversity of ecological time series to gain useful insights and produce accurate forecasts. All course materials (presentations, practical exercises, data files, and commented R scripts) will be provided electronically to participants.

Forecasting species’ responses to change

Using models to anticipate the future is vital to mitigate the impacts of environmental change on ecosystems. Yet most ecological models are one-off attempts to predict what ecosystems might be like in many years or decades. This makes it hard for decision-makers to use these models. It also favours models that are not easily scrutinised and improved. This project aims to employ an iterative cycle to forecast how species occurrences and abundances will change over short timescales, and will analyse forecasts to improve models so that we can continue to learn. This represents a new way of thinking in ecology that, like weather forecasting, has the power to advance our understanding of ecological processes.

The student will be based at The University of Queensland within the School of Veterinary Science, primarily supervised by Dr. Nicholas Clark. The candidate will work with a diverse group of international researchers. Additional support will be given by partners at the Ecological Forecasting Initiative and the Spatial Epidemiology Laboratory, including assistance in computer-based data analysis, model building and scientific communication. The selected student will have the opportunity to work with all partners on this project but will be based at UQ.

This project will help develop the candidate’s skills in critical thinking, project management, data management and analysis, writing and communication. Expected applications of the project are incredibly diverse, meaning the student will be well prepared for a future career in research or with government and non-government land management, biosecurity or conservation agencies.

Harnessing the power of ecological forecasting

Rapidly changing climates and landscape modification are impacting global ecosystems at all micro- and macroecological levels, incurring significant economic and environmental costs. Human encroachment into bushland and habitat alterations are magnifying risks of zoonotic diseases and shifting key conservation targets. Changing temperatures are altering food distributions and influencing reproductive cycles for important fishery species, introducing major uncertainties for vulnerable economies. There is broad consensus among scientists, parliamentarians and decision-makers that anticipating probable future states is vital to mitigate these impacts of environmental change. Ecological forecasting is a fundamental representation of hypothesis-driven science that aims to address this gap by (1) using theory-driven models and observational data to make near-term forecasts (2) falsifying these forecasts against future data to identify critical data / model limitations (3) refining hypotheses and model structures and (4) repeating. This iterative cycle can accelerate learning, drive model improvement and emphasize outputs that are immediately useful for effective planning and resource management. It is no surprise then that ecological forecasting is becoming a key focus in diverse fields including evolutionary biology, ecosystem services and epidemiology. In this talk, I introduce the near-term forecasting cycle and motivate its importance to ecology by providing a set of simple but thought-provoking questions we can ask ourselves whenever we seek to build biologically relevant models and deliver predictions that are more useful to relevant end-users.

State-Space Vector Autoregressions in mvgam

Vector Autoregressions (VAR models), also known as Multivariate Autoregressions (MAR models), offer a way to model delayed and contemporaneous interactions among sets of multiple time series. These models are widely used in econometrics and psychology, among other fields, where they can be analyzed to ask many interesting questions about potential causality or stability. But software to fit these models to real-world time series, which often present as non-Gaussian counts, proportions or even binary observations with measurement error, is lacking. Here I show how to fit VARs in a State-Space format, and how to interrogate the models to ask meaningful questions about interactions and stability, using the mvgam package in R.

Incorporating time-varying seasonality in forecast models

Many time series show repeated seasonal patterns, and fitting models that can capture this seasonality is a major focus of time series forecasting algorithms. There are a lot of useful, established methods to deal with this (i.e. SARIMA, Harmonic regression), but sometimes the seasonal patterns change over time. Fewer time series and forecasting models can handle this feature. This post introduces some strategies for capturing time-varying seasonality and time-varying periodicity in Dynamic Generalized Additive Models, using the mvgam package in R.

Ecological forecasting with R 📦’s {mvgam} and {brms}

Time series analysis and forecasting are standard goals in applied ecology. But most time series courses focus only on traditional forecasting models such as ARIMA or Exponential Smoothing. These models cannot handle features that dominate ecological data, including overdispersion, clustering, missingness, discreteness and nonlinear effects. Using the flexible and powerful Bayesian modelling software Stan, we can now meet this complexity head on. R packages such as {mvgam} and {brms} can build Stan code to specify ecologically appropriate models that include nonlinear effects, random effects and dynamic processes, all with simple interfaces that are familiar to most R users. In this course you will learn how to wrangle, visualize and explore ecological time series. You will also learn to use the {mvgam} and {brms} packages to analyse a diversity of ecological time series to gain useful insights and produce accurate forecasts. All course materials (presentations, practical exercises, data files, and commented R scripts) will be provided electronically to participants.

Time series modeling with Bayesian Dynamic Generalized Additive Models

In this talk I introduce Bayesian Dynamic Generalized Additive Models (DGAMs) and illustrate their advantages for analyzing and forecasting real-world time series. I discuss mvgam, an open-source R package that can fit DGAMs with nonlinear effects, hierarchical effects and dynamic processes to data from a wide variety of observation distributions. These models are especially useful for analysing multiple series, as they can estimate hierarchical smooth functions while learning complex temporal associations with latent vector autoregressive processes or dimension-reduced dynamic factor processes. Because the package uses Hamiltonian Monte Carlo inference through Stan, it is straightforward to create Stan code and all necessary data structures so that additional stochastic elements can be added to suit the user’s bespoke needs. Other key features of {mvgam} are functions to critique models using rolling window forecasts and posterior predictive checks, online data augmentation via a recursive particle filter and graphical tools to visualise probabilistic uncertainties for smooth functions and predictions. I hope show how models that describe real-world complexity, both through nonlinear covariate functions and multi-series dependence, are useful to ask targeted questions about drivers of change.

Ecological forecasting with dynamic GAMs

Time series analysis and forecasting are standard goals in applied ecology. But ecological forecasting is difficult because ecology is complex. The abundances of species, for example, fluctuate for many reasons. Food and shelter availability limit survival. Biotic interactions affect colonization and vital rates. Severe weather events and climate variation alter habitat suitability. These sources of variation make it difficult to understand, let alone predict, ecosystem change. Moreover, most available time series software cannot handle features that dominate ecological data, including overdispersion, clustering, missingness, discreteness and nonlinear effects. In this talk, I will introduce Dynamic Generalized Additive Models (DGAMs) as one solution to meet this complexity. I illustrate a number of models that can be tackled with the mvgam R package, which builds Stan code to specify probabilistic Bayesian models that include nonlinear smooth functions, random effects and dynamic processes, all with a simple interface that is familiar to most R users.

Ecological forecasting with dynamic Generalized Additive Models (DGAMs)

Time series analysis and forecasting are standard goals in applied ecology. But ecological forecasting is difficult because ecology is complex. The abundances of species, for example, fluctuate for many reasons. Food and shelter availability limit survival. Biotic interactions affect colonization and vital rates. Severe weather events and climate variation alter habitat suitability. These sources of variation make it difficult to understand, let alone predict, ecosystem change. Moreover, most available time series software cannot handle features that dominate ecological data, including overdispersion, clustering, missingness, discreteness and nonlinear effects. In this talk, I will introduce Dynamic Generalized Additive Models (DGAMs) as one solution to meet this complexity. I illustrate a number of models that can be tackled with the mvgam R package, which builds Stan code to specify probabilistic Bayesian models that include nonlinear smooth functions, random effects and dynamic processes, all with a simple interface that is familiar to most R users.