Practical Forecasting of Environmental Maps: A Functional Data Approach

Statistics > Methodology [Submitted on 7 Feb 2022 (v1), last revised 17 Jun 2026 (this version, v2)] Title:Practical Forecasting of Environmental Maps: A Functional Data Approach View PDF HTML (experimental)Abstract:Environmental problems are receiving increasing attention in socio-economic and health studies, fostering advances in recording and data collection of related real-life processes. However, traditional tools for data processing are often found too restrictive as they do not account for the rich nature of such data sets. In this paper, we propose a simple statistical perspective on forecasting environmental data collected sequentially over time across some predefined geographic region. We treat such data set as a surface (or functional) time series with a possibly complicated geographical domain. Using techniques from functional data analysis, we develop a forecasting methodology that allows to account for both geographic and temporal dependencies. This methodology allows integration of traditional multivariate techniques to provide forecasts surfaces. We demonstrate the practical value of our approach with a forecasting example of ground-level ozone concentration across Germany, showcasing its effectiveness and potential for broad application. Submission history From: Nazarii Salish [view email][v1] Mon, 7 Feb 2022 16:22:33 UTC (4,818 KB) [v2] Wed, 17 Jun 2026 07:59:12 UTC (8,693 KB) Current browse context: stat.ME References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.