My main research focus is on probabilistic models for time series, this includes modelling, imputation, denoising, filtering and spectral estimation.

My Google Scholar profile can be seen here.See [C19] below and click here for code and demo.

[J12]

[J11]

J. Dunstan, M. Aguirre, M. Bastías, C. Nau, T. A. Glass, and F. Tobar, ‘Predicting nationwide obesity from food sales using machine learning’, *Health Informatics Journal*, p. 1460458219845959, 2019. Available: SAGE.

[J10]

B. Poblete, J. Guzman, J. Maldonado, and F. Tobar, ‘Robust detection of extreme events using Twitter: Worldwide earthquake monitoring’, *IEEE Transactions on Multimedia*, vol. 20, no. 10, pp. 2551–2561, 2018. Available: IEEEXplore

[J9]

F. Tobar, I. Castro, J. Silva, and M. Orchard, ‘Improving battery voltage prediction in an electric bicycle using altitude measurements and kernel adaptive filters’, *Pattern Recognition Letters*, vol. 105, pp. 200–206, 2018. Available: Elsevier

[J8]

D. Cabrera, F. Sancho, M. Cerrada, R.-V. Sánchez, and F. Tobar, ‘Echo state network and variational autoencoder for efficient one-class learning on dynamical systems’, *Journal of Intelligent & Fuzzy Systems.*, vol. 34, no. 6, pp. 3799–3809, 2018. Available: IOS Press

[J7]

F. Tobar, G. Rios, T. Valdivia, and P. Guerrero, ‘Recovering latent signals from a mixture of measurements using a Gaussian process prior’, *IEEE Signal Processing Letters*, vol. 24, no. 2, pp. 231–235, 2017. Available: IEEEXplore, ArXiV

[J6]

F. Tobar and D. Mandic, ‘Design of positive-definite quaternion kernels’, *IEEE Signal Processing Letters*, vol. 22, no. 11, pp. 2117–2121, 2015. Available: IEEEXplore

[J5]

F. Tobar, P. Djurić, and D. Mandic, ‘Unsupervised state-space modeling using reproducing kernels’, *IEEE Trans. on Signal Processing*, vol. 63, no. 19, pp. 5210–5221, 2015. Available: IEEEXplore

[J4]

F. A. Tobar, S. Kung, and D. P. Mandic, ‘Multikernel Least Mean Square Algorithm’, *IEEE Transactions on Neural Networks and Learning Systems*, vol. 25, no. 2, pp. 265–277, 2014. Available: IEEEXplore

[J3]

F. Tobar and D. Mandic, ‘Quaternion reproducing kernel Hilbert spaces: Existence and uniqueness conditionss’, *IEEE Trans. on Inf. Theory*, vol. 60, no. 9, pp. 5736–5749, 2014. Available: IEEEXplore

[J2]

F. Tobar and M. Orchard, ‘Study of financial systems volatility using suboptimal estimation algorithms’, *Studies in Informatics and Control*, vol. 21, no. 1, pp. 59–66, 2012. Available: here

[J1]

M. Orchard, F. Tobar, and G. Vachtsevanos, ‘Outer feedback correction loops in particle filtering-based prognostic algorithms: Statistical performance comparison’, *Studies in Informatics and Control*, vol. 18, no. 4, pp. 295–304, 2009. Available: here

[C21]

F. Tobar, ‘Band-limited Gaussian processes: The Sinc kernel’, in *Advances in Neural Information Processing Systems 32*, 2019, pp. 12728-12738. Available: Paper, Poster, Presentation.

[C20]

C. Valenzuela and F. Tobar, ‘Low-pass filtering as Bayesian inference ’, *Proc. of IEEE ICASSP*, 2019, pp. 3367–3371. Available: arXiv and IEEEXplore

[C19]

F. Tobar, ‘Bayesian Nonparametric Spectral Estimation’, in *Advances in Neural Information Processing Systems 31*, 2018, pp. 10148–10158. Available: Paper, Poster, Spotlight Presentation.

[C18]

G. Rios and F. Tobar, ‘The Box-Cox Gaussian Process’, in *Proc. of the IEEE International Joint Conference on Neural Networks*, 2018, pp. 1–8. Available: IEEEXplore

[C17]

[C16]

F. Tobar, ‘Improving sparsity in kernel adaptive filters using a unit-norm dictionary’, in *Proc. of the 22nd International Conference on Digital Signal Processing (DSP)*, 2017, pp. 1–5. Available: IEEEXplore

[C15]

A. Cuevas, A. Veragua, S. Español, G. Chiang, and F. Tobar, ‘Unsupervised Blue Whale Call Detection Using Multiple Time-Frequency Features’, in *Proc. of IEEE Chilecon*, 2017, pp. 1–6. Available: IEEEXplore

[C14]

I. Castro, C. Silva, and F. Tobar, ‘Initialising kernel adaptive filters via probabilistic inference’, in *Proc. of the 22nd International Conference on Digital Signal Processing (DSP)*, 2017, pp. 1–5. Available: IEEEXplore

[C13]

D. Cabrera, F. Sancho, and F. Tobar, ‘Combining reservoir computing and variational inference for efficient one-class learning on dynamical systems’, in *Proc. of the IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)*, 2017, pp. 57–62. Available: IEEEXplore

[C12]

L. Araya-Hernández, J. Silva, A. Osses, and F. Tobar, ‘A Bayesian mixture-of-Gaussians model for astronomical observations in interferometry’, in *Proc. of IEEE Chilecon*, 2017. Available: IEEEXplore

[C11]

F. Tobar and R. Turner, ‘Modelling time series via automatic learning of basis functions’, in *Proc. of the IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)*, 2016, pp. 1–5. Available: IEEEXplore

[C10]

T. Thanthawaritthisai, F. Tobar, A. Constantinides, and D. Mandic, ‘The widely linear quaternion recursive total least squares’, in *Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 2015, pp. 3357–3361. Available: IEEEXplore

[C9]

F. Tobar and R. Turner, ‘Modelling of complex signals using Gaussian processes’, in *Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 2015, pp. 2209–2213. Available: IEEEXplore

[C8]

F. Tobar, T. Bui, and R. Turner, ‘Learning stationary time series using Gaussian processes with nonparametric kernels’, in *Advances in Neural Information Processing Systems 28*, 2015, pp. 3501--3509. Available: NeurIPS

[C7]

F. Tobar, M. Orchard, D. Mandic, and A. Constantinides, ‘Estimation of financial indices volatility using a model with time-varying parameters’, in *Proc. of IEEE Conference on Computational Intelligence for Financial Engineering Economics (CIFEr)*, 2014, pp. 318–324. Available: IEEEXplore

[C6]

F. Tobar and D. Mandic, ‘A particle filtering based kernel HMM predictor’, in *Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 2014, pp. 7969–7973. Available: IEEEXplore

[C5]

F. Tobar and D. Mandic, ‘The quaternion kernel least squares’, in *Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 2013, pp. 6128–6132. Available: IEEEXplore

[C4]

F. Tobar and D. Mandic, ‘Multikernel least squares estimation’, in *Proc. of the Sensor Signal Processing for Defence Conference(SSPD)*, 2012, pp. 1–5. Available: IEEEXplore

[C3]

F. Tobar, A. Kuh, and D. Mandic, ‘A novel augmented complex valued kernel LMS’, in *Proc. of the 7th IEEE Sensor Array and Multichannel Signal Processing Workshop*, 2012, pp. 473–476. Available: IEEEXplore

[C2]

A. Ahrabian, D. Looney, F. Tobar, J. Hallatt, and D. Mandic, ‘Noise assisted multivariate empirical mode decomposition applied to Doppler radar data’, in *Proc. of the Sensor Signal Processing for Defence Conference(SSPD)*, 2012, pp. 1–4. Available: IEEEXplore

[C1]

F. Tobar, L. Yacher, R. Paredes, and M. Orchard, ‘Anomaly detection in power generation plants using similarity-based modeling and multivariate analysis’, in *Proc. of the American Control Conference (ACC)*, 2011, pp. 1940–1945. Available: IEEEXplore

(by myself)