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Assessing predictability of environmental time series with statistical and machine learning models

https://doi.org/10.1002/env.2864
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/0471721182openalex-snapshot
Finite Mixture Models
inferredcites →https://doi.org/10.1002/ecm.1470openalex-snapshot
A guide to state–space modeling of ecological time series
inferredcites →https://doi.org/10.1002/env.2553openalex-snapshot
Deep echo state networks with uncertainty quantification for spatio‐temporal forecasting
inferredcites →https://doi.org/10.1002/env.2772openalex-snapshot
An illustration of model agnostic explainability methods applied to environmental data
inferredcites →https://doi.org/10.1002/for.3980090203openalex-snapshot
Estimation procedures for structural time series models
inferredcites →https://doi.org/10.1002/sim.6893openalex-snapshot
Nonparametric survival analysis using Bayesian Additive Regression Trees (BART)
inferredcites →https://doi.org/10.1002/sta4.160openalex-snapshot
An ensemble quadratic echo state network for non‐linear spatio‐temporal forecasting
inferredcites →https://doi.org/10.1007/978-3-319-29854-2openalex-snapshot
Introduction to Time Series and Forecasting
inferredcites →https://doi.org/10.1007/978-3-642-35289-8_36openalex-snapshot
A Practical Guide to Applying Echo State Networks
inferredcites →https://doi.org/10.1007/s00521-021-06878-8openalex-snapshot
Prediction of temperature anomaly in Indian Ocean based on autoregressive long short-term memory neural network
inferredcites →https://doi.org/10.1007/s11063-019-10115-8openalex-snapshot
Traffic Signs Detection for Real-World Application of an Advanced Driving Assisting System Using Deep Learning
inferredcites →https://doi.org/10.1007/s13762-020-03079-zopenalex-snapshot
Prediction of CO2 emission from greenhouse to atmosphere with artificial neural networks and deep learning neural networks
inferredcites →https://doi.org/10.1016/j.atmosenv.2019.117130openalex-snapshot
A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5 concentration
inferredcites →https://doi.org/10.1016/j.envsoft.2019.06.014openalex-snapshot
A review of artificial neural network models for ambient air pollution prediction
inferredcites →https://doi.org/10.1016/j.ijforecast.2003.09.015openalex-snapshot
Forecasting seasonals and trends by exponentially weighted moving averages
inferredcites →https://doi.org/10.1016/j.insmatheco.2021.08.003openalex-snapshot
Gamma Mixture Density Networks and their application to modelling insurance claim amounts
inferredcites →https://doi.org/10.1016/j.rama.2017.02.004openalex-snapshot
Integrating Remotely Sensed Imagery and Existing Multiscale Field Data to Derive Rangeland Indicators: Application of Bayesian Additive Regression Trees
inferredcites →https://doi.org/10.1016/s2542-5196(20)30224-2openalex-snapshot
Short-term and long-term health impacts of air pollution reductions from COVID-19 lockdowns in China and Europe: a modelling study
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1038/s41370-020-0246-yopenalex-snapshot
A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication
inferredcites →https://doi.org/10.1038/s41558-022-01377-7openalex-snapshot
Aligning artificial intelligence with climate change mitigation
inferredcites →https://doi.org/10.1038/s42256-022-00481-9openalex-snapshot
The perils of machine learning in designing new chemicals and materials
inferredcites →https://doi.org/10.1080/00031305.2017.1380080openalex-snapshot
Forecasting at Scale
inferredcites →https://doi.org/10.1080/01621459.1998.10473750openalex-snapshot
Bayesian CART Model Search
inferredcites →https://doi.org/10.1080/01621459.2021.1950003openalex-snapshot
Random Forests for Spatially Dependent Data
inferredcites →https://doi.org/10.1109/4235.585893openalex-snapshot
No free lunch theorems for optimization
inferredcites →https://doi.org/10.1109/72.279181openalex-snapshot
Learning long-term dependencies with gradient descent is difficult
inferredcites →https://doi.org/10.1109/9780470544037openalex-snapshot
A Field Guide to Dynamical Recurrent Networks
inferredcites →https://doi.org/10.1109/ijcnn.2017.7966210openalex-snapshot
Recent advances in video-based human action recognition using deep learning: A review
inferredcites →https://doi.org/10.1109/tevc.2005.856205openalex-snapshot
Coevolutionary Free Lunches
inferredcites →https://doi.org/10.1111/j.1467-9868.2007.00587.xopenalex-snapshot
Probabilistic Forecasts, Calibration and Sharpness
inferredcites →https://doi.org/10.1111/j.1467-9892.2009.00643.xopenalex-snapshot
Time series analysis forecasting and control
inferredcites →https://doi.org/10.1111/rssc.12540openalex-snapshot
Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally Reduced Echo State Networks
inferredcites →https://doi.org/10.1145/1273496openalex-snapshot
Proceedings of the 24th international conference on Machine learning
inferredcites →https://doi.org/10.1162/neco.1992.4.3.448openalex-snapshot
A Practical Bayesian Framework for Backpropagation Networks
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-snapshot
Long Short-Term Memory
inferredcites →https://doi.org/10.1175/1520-0434(1997)012<0633:aenoso>2.0.co;2openalex-snapshot
An El Niño–Southern Oscillation Climatology and Persistence (CLIPER) Forecasting Scheme
inferredcites →https://doi.org/10.1198/jasa.2011.tm09771openalex-snapshot
Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing
inferredcites →https://doi.org/10.1214/09-aoas285openalex-snapshot
BART: Bayesian additive regression trees
inferredcites →https://doi.org/10.1214/20-ejs1758openalex-snapshot
Modeling of time series using random forests: Theoretical developments
inferredcites →https://doi.org/10.1214/22-aoas1683openalex-snapshot
Calibration of SpatioTemporal forecasts from citizen science urban air pollution data with sparse recurrent neural networks
inferredcites →https://doi.org/10.1214/aos/1013203451openalex-snapshot
Greedy function approximation: A gradient boosting machine.
inferredcites →https://doi.org/10.18637/jss.v027.i03openalex-snapshot
Automatic Time Series Forecasting: The forecast Package for R
inferredcites →https://doi.org/10.18637/jss.v097.i01openalex-snapshot
Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package
inferredcites →https://doi.org/10.1890/02-0670openalex-snapshot
META-ANALYSIS OF ANIMAL MOVEMENT USING STATE-SPACE MODELS
inferredcites →https://doi.org/10.21105/joss.03780openalex-snapshot
RandomForestsGLS: An R package for Random Forests for dependent data
inferredcites →https://doi.org/10.2139/ssrn.4001163openalex-snapshot
Machine Learning for Improving Accuracy and Utility of Low-Cost Environmental Air Pollution Sensor Networks for Probabilistic Spatial Exposure Assessment
inferredcites →https://doi.org/10.2307/2530946openalex-snapshot
Classification and Regression Trees.
inferredcites →https://doi.org/10.3390/e21020184openalex-snapshot
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
inferredcites →https://doi.org/10.4249/scholarpedia.2330openalex-snapshot
Echo state network
inferredcites →https://doi.org/10.48550/arxiv.1506.02142openalex-snapshot
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
inferredcites →https://doi.org/10.5281/zenodo.1075059openalex-snapshot
Machine Learning Methods For Environmental Monitoring And Flood Protection

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/env.2881openalex-snapshot
Bias correction of daily precipitation from climate models, using the Q‐GAM method
inferred← citeshttps://doi.org/10.1002/env.2898openalex-snapshot
Discussion on “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models” by Bonas et al.
inferred← citeshttps://doi.org/10.1002/env.2900openalex-snapshot
Discussion on Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models
inferred← citeshttps://doi.org/10.1002/env.70000openalex-snapshot
“Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”
inferred← citeshttps://doi.org/10.1002/env.70001openalex-snapshot
Discussion on “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”
inferred← citeshttps://doi.org/10.1002/env.70003openalex-snapshot
Discussion on Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models
inferred← citeshttps://doi.org/10.1002/env.70017openalex-snapshot
Computational Benchmark Study in Spatio‐Temporal Statistics With a Hands‐On Guide to Optimise R
inferred← citeshttps://doi.org/10.1002/env.70070openalex-snapshot
Modeling the Spatial Interplay Between Migration and Environmental Conditions
inferred← citeshttps://doi.org/10.1002/env.70108openalex-snapshot
Capturing Aleatoric Uncertainty in Climate Models
inferred← citeshttps://doi.org/10.1007/s44292-025-00072-4openalex-snapshot
A hierarchical ensemble approach for multi-country PM10 forecasting using LightGBM and residual neural network
inferred← citeshttps://doi.org/10.1016/j.teler.2026.100307openalex-snapshot
Understanding metaverse adoption and sustainability across students and educators: Evidence from the diffusion of innovation model
inferred← citeshttps://doi.org/10.1080/26941899.2025.2565243openalex-snapshot
Hierarchical Count Echo State Network Models with Application to Graduate Student Enrollments
inferred← citeshttps://doi.org/10.1177/1471082x251395447openalex-snapshot
Bayesian multivariate semi-Markov-switching mixed data sampling
inferred← citeshttps://doi.org/10.21203/rs.3.rs-10230085/v1openalex-snapshot
Modeling and Forecasting Meat Production in Somalia, 1961–2024: A Comparative Analysis of Single and Hybrid Time Series Models
inferred← citeshttps://doi.org/10.3390/math13060894openalex-snapshot
Learning High-Dimensional Chaos Based on an Echo State Network with Homotopy Transformation
inferred← citeshttps://doi.org/10.4018/979-8-3373-6259-5.ch001openalex-snapshot
Environmetrics and Ethical Hazard Analysis in Teaching Environmental and Risk Statistics in the AI Era
inferred← citeshttps://doi.org/10.62051/pb4xdx35openalex-snapshot
Research on a Time Series Forecasting Model Based on Multiple Regression and Polynomial Fitting
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