718
Views
0
CrossRef citations to date
0
Altmetric
Article

Hybrid Forecasting for Functional Time Series of Dissolved Oxygen Profiles

ORCID Icon, ORCID Icon & ORCID Icon
Article: 2152401 | Received 06 Jun 2022, Accepted 22 Nov 2022, Published online: 08 Feb 2023

References

  • Andersen MR, de Eyto E, Dillane M, Poole R, Jennings E. 2020. 13 Years of storms: an analysis of the effects of storms on lake physics on the atlantic fringe of Europe. Water. 12(2):318.
  • Angamuthu Chinnathambi R, Mukherjee A, Campion M, Salehfar H, Hansen TM, Lin J, Ranganathan P. 2018. A multi-stage price forecasting model for day-ahead electricity markets. Forecasting. 1(1):26–46.
  • Aue A, Norinho DD, Hörmann S. 2015. On the prediction of stationary functional time series. J Am Stat Assoc. 110(509):378–392.
  • Banks JL, Ross DJ, Keough MJ, Eyre BD, Macleod CK. 2012. Measuring hypoxia induced metal release from highly contaminated estuarine sediments during a 40 day laboratory incubation experiment. Sci Total Environ. 420:229–237.
  • Barua S, Elhalawani H, Volpe S, Al Feghali KA, Yang P, Ng SP, Elgohari B, Granberry RC, Mackin DS, Gunn GB, et al. 2021. Computed tomography radiomics kinetics as early imaging correlates of osteoradionecrosis in oropharyngeal cancer patients. Front Artif Intell. 4:618469.
  • Carey CC, Woelmer WM, Lofton ME, Figueiredo RJ, Bookout BJ, Corrigan RS, Daneshmand V, Hounshell AG, Howard DW, Lewis ASL, et al. 2022. Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting. Inland Waters. 12(1):107–120.
  • Chin CK, Mat D. A b A, Saleh AY. 2021. Hybrid of convolutional neural network algorithm and autoregressive integrated moving average model for skin cancer classification among Malaysian. IJ-AI. 10(3):707–716.
  • Chiou J-M, Chen Y-T, Yang Y-F. 2014. Multivariate functional principal component analysis: a normalization approach. Statistica Sinica. 24(4):1571–1596.
  • Chipman HA, George EI, McCulloch RE. 2010. BART: Bayesian additive regression trees. Ann Appl Stat. 4(1):266–298.
  • da Rosa CE, de Souza MS, Yunes J, a. S, Proença LAO, M, Nery LE, Monserrat JM. 2005. Cyanobacterial blooms in estuarine ecosystems: characteristics and effects on Laeonereis acuta (Polychaeta, Nereididae). Mar Pollut Bull. 50(9):956–964.
  • Das P, Naganna SR, Deka PC, Pushparaj J. 2020. Hybrid wavelet packet machine learning approaches for drought modeling. Environ Earth Sci. 79(10):1–18.
  • Durell L, Scott JT, Nychka D, Hering AS. 2022. Functional forecasting of dissolved oxygen in high-frequency vertical lake profiles. Environmetrics. [16 p.]. doi:10.1002/env.2765
  • Fard AK, Akbari-Zadeh M-R. 2014. A hybrid method based on wavelet, ANN and ARIMA model for short-term load forecasting. J Exp Theor Artif Intell. 26(2):167–182.
  • Ferré L, Villa N. 2006. Multilayer perceptron with functional inputs: an inverse regression approach. Scand J Stat. 33(4):807–823.
  • Gabry J, Ali I, Brilleman S, Buros Novik J, AstraZeneca, Trustees of Columbia University, Wood S, R Core Development Team, Bates D, Maechler M, et al. rstanarm: Bayesian applied regression modeling via Stan. R package version 2.21.3. https://mc-stan.org/rstanarm/
  • Gelman A, Hill J. 2007. Data analysis using regression and multilevel/hierarchical models. 1st ed. New York: Cambridge University Press.
  • Happ C, Greven S. 2018. Multivariate functional principal component analysis for data observed on different (dimensional) domains. J Am Stat Assoc. 113(522):649–659.
  • Hastie T, Tibshirani R, Friedman J. 2009. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. Springer Series in Statistics. New York: Springer-Verlag.
  • He J, Hahn R, Lopes H, Herren A. 2022. bayeslm: efficient sampling for Gaussian linear regression with arbitrary priors. R package version 1.0.1. https://cran.ism.ac.jp/web/packages/bayeslm/bayeslm.pdf
  • Hyndman RJ, Ullah MS. 2007. Robust forecasting of mortality and fertility rates: a functional data approach. Comput Stat Data Anal. 51(10):4942–4956.
  • Ju X, Salibián-Barrera M. 2021. Tree-based boosting with functional data. arXiv:2109.02989 [stat].
  • Karakaya N, Evrendilek F, Güngör K. 2011. Modeling and validating long-term dynamics of diel dissolved oxygen with particular reference to pH in a temperate shallow lake (Turkey). Clean Soil Air Water. 39(11):966–971.
  • Kokoszka P, Reimherr M. 2017. Introduction to functional data analysis. Boca Raton (FL): Chapman and Hall/CRC.
  • Lane SE, Robinson AP. 2011. An alternative objective function for fitting regression trees to functional response variables. Comput Stat Data Anal. 55(9):2557–2567.
  • Lee JS, Zakeri IF, Butte NF. 2017. Functional data analysis of sleeping energy expenditure. PLOS One. 12(5):e0177286.
  • Li H, Xiao G, Xia T, Tang YY, Li L. 2014. Hyperspectral image classification using functional data analysis. IEEE Trans Cybern. 44(9):1544–1555.
  • Lin Z, Zhu H. 2019. MFPCA: multiscale functional principal component analysis. Proc AAAI Conf Artif Intell, 33(01):4320–4327.
  • López M, Martínez J, Matí–As JM, Taboada J, Vilán JA. 2010. Functional classification of ornamental stone using machine learning techniques. J Comput Appl Math. 234(4):1338–1345.
  • Meng Y, Liang J, Qian Y. 2016. Comparison study of orthonormal representations of functional data in classification. Knowledge Based Syst. 97:224–236.
  • Mesman JP, Ayala AI, Adrian R, De Eyto E, Frassl MA, Goyette S, Kasparian J, Perroud M, Stelzer JAA, Pierson DC, et al. 2020. Performance of one-dimensional hydrodynamic lake models during short-term extreme weather events. Environ Model Software. 133:104852.
  • Mohammadi B, Mehdizadeh S, Ahmadi F, Lien NTT, Linh NTT, Pham QB. 2021. Developing hybrid time series and artificial intelligence models for estimating air temperatures. Stoch Environ Res Risk Assess. 35(6):1189–1204.
  • Nerini D, Ghattas B. 2007. Classifying densities using functional regression trees: applications in oceanology. Comput Stat Data Anal. 51(10):4984–4993.
  • Newhart KB, Marks CA, Rauch-Williams T, Cath TY, Hering AS. 2020. Hybrid statistical-machine learning ammonia forecasting in continuous activated sludge treatment for improved process control. J Water Process Eng. 37:101389.
  • Perdices D, de Vergara JEL, Ramos J. 2021. Deep-FDA: using functional data analysis and neural networks to characterize network services time series. IEEE Trans Netw Serv Manage. 18(1):986–999.
  • Pesaresi S, Mancini A, Quattrini G, Casavecchia S. 2020. Mapping Mediterranean forest plant associations and habitats with functional principal component analysis using Landsat 8 NDVI time series. Remote Sens. 12(7):1132.
  • Rahman R, Dhruba SR, Ghosh S, Pal R. 2019. Functional random forest with applications in dose-response predictions. Sci Rep. 9(1):1628.
  • Rao AR, Reimherr M. 2021. Modern non-linear function-on-function regression. arXiv:2107.14151 [cs, stat].
  • Rao AR, Wang Q, Wang H, Khorasgani H, Gupta C. 2020. Spatio-temporal functional neural networks. 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA);81–89.
  • Rossi F, Conan-Guez B. 2005. Functional multi-layer perceptron: a non-linear tool for functional data analysis. Neural Netw. 18(1):45–60.
  • Rossi F, Conan-Guez B. 2006. Theoretical properties of projection based multilayer perceptrons with functional inputs. Neural Process Lett. 23(1):55–70.
  • Rossi F, Conan-Guez B, Fleuret F. 2002. Functional data analysis with multi layer perceptrons. Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No.02CH37290), Vol. 3; p. 2843–2848.
  • Rossi F, Delannay N, Conan-Guez B, Verleysen M. 2005. Representation of functional data in neural networks. Neurocomputing. 64:183–210.
  • Rossi F, Villa N. 2006. Support vector machine for functional data classification. Neurocomputing. 69(7-9):730–742.
  • Siljic Tomic A, Antanasijevic D, Ristic M, Peric-Grujic A, Pocajt V. 2018. A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: inter- and extrapolation performance with inputs’ significance analysis. Sci Total Environ. 610–611:1038–1046.
  • Starling JE, Murray JS, Carvalho CM, Bukowski RK, Scott JG. 2020. BART with targeted smoothing: an analysis of patient-specific stillbirth risk. Ann Appl Stat. 14(1):28–50.
  • Tengberg A, Hovdenes J, Andersson HJ, Brocandel O, Diaz R, Hebert D, Arnerich T, Huber C, Körtzinger A, Khripounoff A, et al. 2006. Evaluation of a lifetime-based optode to measure oxygen in aquatic systems. Limnol Oceanogr Methods. 4(2):7–17.
  • Thind B, Multani K, Cao J. 2022. Deep learning with functional inputs. J Comput Graph Stat.
  • Thomas RQ, Figueiredo RJ, Daneshmand V, Bookout BJ, Puckett LK, Carey CC. 2020. A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time. Water Resour Res. 56(11):e2019WR026138.
  • Valera M, Walter RK, Bailey BA, Castillo JE. 2020. Machine learning based predictions of dissolved oxygen in a small coastal embayment. JMSE. 8(12):1007.
  • Wang Q, Farahat A, Gupta C, Zheng S. 2021. Deep time series models for scarce data. Neurocomputing. 456:504–518.
  • Wang Q, Wang H, Gupta C, Rao AR, Khorasgani H. 2020. A non-linear function-on-function model for regression with time series data. 2020 IEEE International Conference on Big Data (Big Data); pages 232–239.
  • Wang Q, Zheng S, Farahat A, Serita S, Gupta C. 2019. Remaining useful life estimation using functional data analysis. 2019 IEEE International Conference on Prognostics and Health Management (ICPHM); 1–8.
  • Wang Q, Zheng S, Farahat A, Serita S, Saeki T, Gupta C. 2019. Multilayer perceptron for sparse functional data. 2019 International Joint Conference on Neural Networks (IJCNN); p. 1–10.
  • Woelmer WM, Thomas RQ, Lofton ME, McClure RP, Wander HL, Carey CC. 2022. Near-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability. Ecol Appl. 32(7):e2642.
  • Yao J, Mueller J, Wang JL. 2021. Deep learning for functional data analysis with adaptive basis layers. International Conference on Machine Learning; p. 11898–11908.
  • Yu Y, Lambert D. 1999. Fitting trees to functional data, with an application to time-of-day patterns. J Comput Graph Stat. 8(4):749–762.