no code implementations • 5 Sep 2023 • Trent Spears, Stefan Zohren, Stephen Roberts
We study an empirical trading strategy respectful of transaction costs, and demonstrate performance over a long history of 29 years, for both a linear and a non-linear state space model.
no code implementations • 23 Aug 2023 • Xingyue, Pu, Stefan Zohren, Stephen Roberts, Xiaowen Dong
Network momentum provides a novel type of risk premium, which exploits the interconnections among assets in a financial network to predict future returns.
no code implementations • 22 Aug 2023 • Xingyue, Pu, Stephen Roberts, Xiaowen Dong, Stefan Zohren
We investigate the concept of network momentum, a novel trading signal derived from momentum spillover across assets.
no code implementations • 22 Jul 2023 • Lawrence Wang, Stephen Roberts
Modern neural networks are undeniably successful.
no code implementations • 7 Jul 2023 • Tom Liu, Stephen Roberts, Stefan Zohren
We introduce Deep Inception Networks (DINs), a family of Deep Learning models that provide a general framework for end-to-end systematic trading strategies.
no code implementations • 24 Jun 2023 • John Williams, Stephen Roberts
We propose a new regularization scheme for the optimization of deep learning architectures, G-TRACER ("Geometric TRACE Ratio"), which promotes generalization by seeking flat minima, and has a sound theoretical basis as an approximation to a natural-gradient descent based optimization of a generalized Bayes objective.
1 code implementation • 20 Feb 2023 • Wee Ling Tan, Stephen Roberts, Stefan Zohren
We introduce Spatio-Temporal Momentum strategies, a class of models that unify both time-series and cross-sectional momentum strategies by trading assets based on their cross-sectional momentum features over time.
no code implementations • 31 Jan 2023 • Trent Spears, Stefan Zohren, Stephen Roberts
We show a relevant, modern case of incorporating machine learning model-derived view and uncertainty estimates, and the impact on portfolio allocation, with an example subsuming Arbitrage Pricing Theory.
1 code implementation • 15 Dec 2022 • Jobie Budd, Kieran Baker, Emma Karoune, Harry Coppock, Selina Patel, Ana Tendero Cañadas, Alexander Titcomb, Richard Payne, David Hurley, Sabrina Egglestone, Lorraine Butler, Jonathon Mellor, George Nicholson, Ivan Kiskin, Vasiliki Koutra, Radka Jersakova, Rachel A. McKendry, Peter Diggle, Sylvia Richardson, Björn W. Schuller, Steven Gilmour, Davide Pigoli, Stephen Roberts, Josef Packham, Tracey Thornley, Chris Holmes
The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date.
1 code implementation • 15 Dec 2022 • Harry Coppock, George Nicholson, Ivan Kiskin, Vasiliki Koutra, Kieran Baker, Jobie Budd, Richard Payne, Emma Karoune, David Hurley, Alexander Titcomb, Sabrina Egglestone, Ana Tendero Cañadas, Lorraine Butler, Radka Jersakova, Jonathon Mellor, Selina Patel, Tracey Thornley, Peter Diggle, Sylvia Richardson, Josef Packham, Björn W. Schuller, Davide Pigoli, Steven Gilmour, Stephen Roberts, Chris Holmes
Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status.
no code implementations • 21 Aug 2022 • Daniel Poh, Stephen Roberts, Stefan Zohren
Cross-sectional strategies are a classical and popular trading style, with recent high performing variants incorporating sophisticated neural architectures.
no code implementations • 13 May 2022 • Björn W. Schuller, Anton Batliner, Shahin Amiriparian, Christian Bergler, Maurice Gerczuk, Natalie Holz, Pauline Larrouy-Maestri, Sebastian P. Bayerl, Korbinian Riedhammer, Adria Mallol-Ragolta, Maria Pateraki, Harry Coppock, Ivan Kiskin, Marianne Sinka, Stephen Roberts
The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Vocalisations and Stuttering Sub-Challenges, a classification on human non-verbal vocalisations and speech has to be made; the Activity Sub-Challenge aims at beyond-audio human activity recognition from smartwatch sensor data; and in the Mosquitoes Sub-Challenge, mosquitoes need to be detected.
3 code implementations • 16 Dec 2021 • Kieran Wood, Sven Giegerich, Stephen Roberts, Stefan Zohren
We introduce the Momentum Transformer, an attention-based deep-learning architecture, which outperforms benchmark time-series momentum and mean-reversion trading strategies.
no code implementations • 7 Dec 2021 • Martin Tegner, Stephen Roberts
Local volatility is a versatile option pricing model due to its state dependent diffusion coefficient.
1 code implementation • 21 Oct 2021 • Shaan Desai, Marios Mattheakis, Hayden Joy, Pavlos Protopapas, Stephen Roberts
In this study, we present a general framework for transfer learning PINNs that results in one-shot inference for linear systems of both ordinary and partial differential equations.
no code implementations • ICLR 2022 • Scott Cameron, Tyron Cameron, Arnu Pretorius, Stephen Roberts
Stochastic differential equations provide a rich class of flexible generative models, capable of describing a wide range of spatio-temporal processes.
1 code implementation • 16 Jul 2021 • Shaan Desai, Marios Mattheakis, David Sondak, Pavlos Protopapas, Stephen Roberts
In this study, we address the challenge of learning from such non-autonomous systems by embedding the port-Hamiltonian formalism into neural networks, a versatile framework that can capture energy dissipation and time-dependent control forces.
no code implementations • NeurIPS 2021 • Jack Parker-Holder, Vu Nguyen, Shaan Desai, Stephen Roberts
Despite a series of recent successes in reinforcement learning (RL), many RL algorithms remain sensitive to hyperparameters.
no code implementations • 4 Jun 2021 • Lewis Smith, Joost van Amersfoort, Haiwen Huang, Stephen Roberts, Yarin Gal
ResNets constrained to be bi-Lipschitz, that is, approximately distance preserving, have been a crucial component of recently proposed techniques for deterministic uncertainty quantification in neural models.
2 code implementations • 28 May 2021 • Kieran Wood, Stephen Roberts, Stefan Zohren
Back-testing our model over the period 1995-2020, the addition of the CPD module leads to an improvement in Sharpe ratio of one-third.
1 code implementation • 20 May 2021 • Daniel Poh, Bryan Lim, Stefan Zohren, Stephen Roberts
The performance of a cross-sectional currency strategy depends crucially on accurately ranking instruments prior to portfolio construction.
no code implementations • ICLR Workshop SSL-RL 2021 • Philip J. Ball, Cong Lu, Jack Parker-Holder, Stephen Roberts
Reinforcement learning from large-scale offline datasets provides us with the ability to learn policies without potentially unsafe or impractical exploration.
1 code implementation • 7 Apr 2021 • Andrea Patane, Arno Blaas, Luca Laurenti, Luca Cardelli, Stephen Roberts, Marta Kwiatkowska
Gaussian processes (GPs) enable principled computation of model uncertainty, making them attractive for safety-critical applications.
no code implementations • 13 Dec 2020 • Daniel Poh, Bryan Lim, Stefan Zohren, Stephen Roberts
The success of a cross-sectional systematic strategy depends critically on accurately ranking assets prior to portfolio construction.
1 code implementation • 7 Aug 2020 • Kyriakos Polymenakos, Nikitas Rontsis, Alessandro Abate, Stephen Roberts
SafePILCO is a software tool for safe and data-efficient policy search with reinforcement learning.
no code implementations • 31 Jul 2020 • Trent Spears, Stefan Zohren, Stephen Roberts
In this work we show that prediction uncertainty estimates gleaned from deep learning models can be useful inputs for influencing the relative allocation of risk capital across trades.
no code implementations • 14 Jul 2020 • Matthew Willetts, Xenia Miscouridou, Stephen Roberts, Chris Holmes
Successfully training Variational Autoencoders (VAEs) with a hierarchy of discrete latent variables remains an area of active research.
no code implementations • NeurIPS 2020 • Alexander Camuto, Matthew Willetts, Umut Şimşekli, Stephen Roberts, Chris Holmes
We study the regularisation induced in neural networks by Gaussian noise injections (GNIs).
no code implementations • 14 Jul 2020 • Alexander Camuto, Matthew Willetts, Stephen Roberts, Chris Holmes, Tom Rainforth
We make inroads into understanding the robustness of Variational Autoencoders (VAEs) to adversarial attacks and other input perturbations.
no code implementations • 21 Jun 2020 • Aldo Pacchiano, Philip J. Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts
The principle of optimism in the face of uncertainty is prevalent throughout sequential decision making problems such as multi-armed bandits and reinforcement learning (RL).
1 code implementation • 16 Jun 2020 • Diego Granziol, Stefan Zohren, Stephen Roberts
Whilst the linear scaling for stochastic gradient descent has been derived under more restrictive conditions, which we generalise, the square root scaling rule for adaptive optimisers is, to our knowledge, completely novel.
2 code implementations • 27 May 2020 • Zihao Zhang, Stefan Zohren, Stephen Roberts
We adopt deep learning models to directly optimise the portfolio Sharpe ratio.
1 code implementation • 28 Apr 2020 • Shaan Desai, Marios Mattheakis, Stephen Roberts
Using this framework we introduce Variational Integrator Graph Networks - a novel method that unifies the strengths of existing approaches by combining an energy constraint, high-order symplectic variational integrators, and graph neural networks.
1 code implementation • IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020 • Shuyu Lin, Ronald Clark, Robert Birke, Sandro Schönborn, Niki Trigoni, Stephen Roberts
In this work, we propose a VAE-LSTM hybrid model as an unsupervised approach for anomaly detection in time series.
no code implementations • 8 Apr 2020 • Jaleh Zand, Stephen Roberts
Generative Adversarial Networks (GANs) have gained significant attention in recent years, with impressive applications highlighted in computer vision in particular.
no code implementations • 2 Mar 2020 • Diego Granziol, Xingchen Wan, Samuel Albanie, Stephen Roberts
We analyse and explain the increased generalisation performance of iterate averaging using a Gaussian process perturbation model between the true and batch risk surface on the high dimensional quadratic.
no code implementations • 18 Feb 2020 • Alexander Camuto, Matthew Willetts, Brooks Paige, Chris Holmes, Stephen Roberts
Separating high-dimensional data like images into independent latent factors, i. e independent component analysis (ICA), remains an open research problem.
no code implementations • ICML 2020 • Philip Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts
Model-Based Reinforcement Learning (MBRL) offers a promising direction for sample efficient learning, often achieving state of the art results for continuous control tasks.
2 code implementations • NeurIPS 2020 • Jack Parker-Holder, Vu Nguyen, Stephen Roberts
A recent solution to this problem is Population Based Training (PBT) which updates both weights and hyperparameters in a single training run of a population of agents.
2 code implementations • NeurIPS 2020 • Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts
Exploration is a key problem in reinforcement learning, since agents can only learn from data they acquire in the environment.
no code implementations • 23 Jan 2020 • Bryan Lim, Stefan Zohren, Stephen Roberts
Detecting changes in asset co-movements is of much importance to financial practitioners, with numerous risk management benefits arising from the timely detection of breakdowns in historical correlations.
1 code implementation • 14 Jan 2020 • Ivan Kiskin, Adam D. Cobb, Lawrence Wang, Stephen Roberts
Mosquitoes are the only known vector of malaria, which leads to hundreds of thousands of deaths each year.
no code implementations • ICLR 2020 • Diego Granziol, Timur Garipov, Dmitry Vetrov, Stefan Zohren, Stephen Roberts, Andrew Gordon Wilson
This approach is an order of magnitude faster than state-of-the-art methods for spectral visualization, and can be generically used to investigate the spectral properties of matrices in deep learning.
no code implementations • 19 Dec 2019 • Diego Granziol, Robin Ru, Stefan Zohren, Xiaowen Dong, Michael Osborne, Stephen Roberts
Graph spectral techniques for measuring graph similarity, or for learning the cluster number, require kernel smoothing.
no code implementations • 4 Dec 2019 • Samuel Kessler, Vu Nguyen, Stefan Zohren, Stephen Roberts
We place an Indian Buffet process (IBP) prior over the structure of a Bayesian Neural Network (BNN), thus allowing the complexity of the BNN to increase and decrease automatically.
no code implementations • 29 Nov 2019 • Kyriakos Polymenakos, Luca Laurenti, Andrea Patane, Jan-Peter Calliess, Luca Cardelli, Marta Kwiatkowska, Alessandro Abate, Stephen Roberts
Gaussian Processes (GPs) are widely employed in control and learning because of their principled treatment of uncertainty.
no code implementations • 22 Nov 2019 • Zihao Zhang, Stefan Zohren, Stephen Roberts
We adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts.
no code implementations • 25 Sep 2019 • Matthew Willetts, Alexander Camuto, Stephen Roberts, Chris Holmes
This paper is concerned with the robustness of VAEs to adversarial attacks.
no code implementations • 25 Sep 2019 • Matthew Willetts, Alexander Camuto, Stephen Roberts, Chris Holmes
We develop a new method for regularising neural networks.
no code implementations • 25 Sep 2019 • Matthew Willetts, Stephen Roberts, Chris Holmes
In clustering we normally output one cluster variable for each datapoint.
no code implementations • 9 Sep 2019 • Shuyu Lin, Stephen Roberts, Niki Trigoni, Ronald Clark
A trade-off exists between reconstruction quality and the prior regularisation in the Evidence Lower Bound (ELBO) loss that Variational Autoencoder (VAE) models use for learning.
no code implementations • 3 Jun 2019 • Diego Granziol, Binxin Ru, Stefan Zohren, Xiaowen Doing, Michael Osborne, Stephen Roberts
Efficient approximation lies at the heart of large-scale machine learning problems.
no code implementations • ICLR 2021 • Matthew Willetts, Alexander Camuto, Tom Rainforth, Stephen Roberts, Chris Holmes
We make significant advances in addressing this issue by introducing methods for producing adversarially robust VAEs.
1 code implementation • 28 May 2019 • Arno Blaas, Andrea Patane, Luca Laurenti, Luca Cardelli, Marta Kwiatkowska, Stephen Roberts
We apply our method to investigate the robustness of GPC models on a 2D synthetic dataset, the SPAM dataset and a subset of the MNIST dataset, providing comparisons of different GPC training techniques, and show how our method can be used for interpretability analysis.
no code implementations • 23 May 2019 • Bryan Lim, Stefan Zohren, Stephen Roberts
Despite recent innovations in network architectures and loss functions, training RNNs to learn long-term dependencies remains difficult due to challenges with gradient-based optimisation methods.
1 code implementation • 9 Apr 2019 • Bryan Lim, Stefan Zohren, Stephen Roberts
While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule.
no code implementations • ICLR Workshop DeepGenStruct 2019 • Shuyu Lin, Ronald Clark, Robert Birke, Niki Trigoni, Stephen Roberts
In this paper, we present a new generative model for learning latent embeddings.
no code implementations • 22 Mar 2019 • Daniel Poh, Stephen Roberts, Martin Tegnér
Secondly, in demonstrating that our model-based strategy outperforms the comparator, and can thus be employed for effective hedging in electricity markets.
no code implementations • 16 Feb 2019 • Shuyu Lin, Ronald Clark, Robert Birke, Niki Trigoni, Stephen Roberts
Variational Auto-encoders (VAEs) have been very successful as methods for forming compressed latent representations of complex, often high-dimensional, data.
1 code implementation • 23 Jan 2019 • Bryan Lim, Stefan Zohren, Stephen Roberts
Testing this on three real-world time series datasets, we demonstrate that the decoupled representations learnt not only improve the accuracy of one-step-ahead forecasts while providing realistic uncertainty estimates, but also facilitate multistep prediction through the separation of encoder stages.
no code implementations • 26 Dec 2018 • Babak Mahdavi-Damghani, Konul Mustafayeva, Stephen Roberts, Cristin Buescu
With the recent rise of Machine Learning as a candidate to partially replace classic Financial Mathematics methodologies, we investigate the performances of both in solving the problem of dynamic portfolio optimization in continuous-time, finite-horizon setting for a portfolio of two assets that are intertwined.
no code implementations • 12 Dec 2018 • Wolfgang Fruehwirt, Adam D. Cobb, Martin Mairhofer, Leonard Weydemann, Heinrich Garn, Reinhold Schmidt, Thomas Benke, Peter Dal-Bianco, Gerhard Ransmayr, Markus Waser, Dieter Grossegger, Pengfei Zhang, Georg Dorffner, Stephen Roberts
As societies around the world are ageing, the number of Alzheimer's disease (AD) patients is rapidly increasing.
1 code implementation • 25 Nov 2018 • Jack Fitzsimons, Michael Osborne, Stephen Roberts
Group fairness is an important concern for machine learning researchers, developers, and regulators.
1 code implementation • 8 Nov 2018 • Samuel Kessler, Arnold Salas, Vincent W. C. Tan, Stefan Zohren, Stephen Roberts
We introduce a novel framework for the estimation of the posterior distribution over the weights of a neural network, based on a new probabilistic interpretation of adaptive optimisation algorithms such as AdaGrad and Adam.
1 code implementation • 29 Oct 2018 • Matthew Willetts, Aiden Doherty, Stephen Roberts, Chris Holmes
We introduce 'semi-unsupervised learning', a problem regime related to transfer learning and zero-shot learning where, in the training data, some classes are sparsely labelled and others entirely unlabelled.
no code implementations • 10 Oct 2018 • Jack Fitzsimons, AbdulRahman Al Ali, Michael Osborne, Stephen Roberts
Fairness, through its many forms and definitions, has become an important issue facing the machine learning community.
4 code implementations • 10 Aug 2018 • Zihao Zhang, Stefan Zohren, Stephen Roberts
We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities.
Computational Finance
no code implementations • 13 Jul 2018 • Martin Tegner, Benjamin Bloem-Reddy, Stephen Roberts
We consider the problem of inferring a latent function in a probabilistic model of data.
no code implementations • 18 Apr 2018 • Diego Granziol, Binxin Ru, Stefan Zohren, Xiaowen Dong, Michael Osborne, Stephen Roberts
Graph spectra have been successfully used to classify network types, compute the similarity between graphs, and determine the number of communities in a network.
1 code implementation • 26 Mar 2018 • Bernardo Pérez Orozco, Gabriele Abbati, Stephen Roberts
In this work, we directly tackle this task with a novel, fully end-to-end deep learning method for time series forecasting.
no code implementations • 24 Mar 2018 • Mariano Chouza, Stephen Roberts, Stefan Zohren
Besides complementing our analytical findings with numerical results from simulated Gaussian random fields, we also compare it to loss functions obtained from optimisation problems on synthetic and real data sets by proposing a "black box" random field toy-model for a deep neural network loss function.
no code implementations • 21 Feb 2018 • Diego Granziol, Edward Wagstaff, Bin Xin Ru, Michael Osborne, Stephen Roberts
Evaluating the log determinant of a positive definite matrix is ubiquitous in machine learning.
1 code implementation • 15 Dec 2017 • Kyriakos Polymenakos, Alessandro Abate, Stephen Roberts
We propose a method to optimise the parameters of a policy which will be used to safely perform a given task in a data-efficient manner.
no code implementations • 7 Dec 2017 • Yunpeng Li, Ivan Kiskin, Davide Zilli, Marianne Sinka, Henry Chan, Kathy Willis, Stephen Roberts
Environmental acoustic sensing involves the retrieval and processing of audio signals to better understand our surroundings.
no code implementations • 1 Dec 2017 • Oliver Bent, Sekou L. Remy, Stephen Roberts, Aisha Walcott-Bryant
The task of decision-making under uncertainty is daunting, especially for problems which have significant complexity.
no code implementations • 22 Nov 2017 • Wolfgang Fruehwirt, Matthias Gerstgrasser, Pengfei Zhang, Leonard Weydemann, Markus Waser, Reinhold Schmidt, Thomas Benke, Peter Dal-Bianco, Gerhard Ransmayr, Dieter Grossegger, Heinrich Garn, Gareth W. Peters, Stephen Roberts, Georg Dorffner
The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations.
no code implementations • 16 Nov 2017 • Yunpeng Li, Davide Zilli, Henry Chan, Ivan Kiskin, Marianne Sinka, Stephen Roberts, Kathy Willis
Mosquitoes are a major vector for malaria, causing hundreds of thousands of deaths in the developing world each year.
no code implementations • 8 Sep 2017 • Diego Granziol, Stephen Roberts
The ability of many powerful machine learning algorithms to deal with large data sets without compromise is often hampered by computationally expensive linear algebra tasks, of which calculating the log determinant is a canonical example.
no code implementations • 15 May 2017 • Ivan Kiskin, Bernardo Pérez Orozco, Theo Windebank, Davide Zilli, Marianne Sinka, Kathy Willis, Stephen Roberts
The huge advances enjoyed by many application domains in recent years have been fuelled by the use of deep learning architectures trained on large data sets.
1 code implementation • 24 Apr 2017 • Jack Fitzsimons, Diego Granziol, Kurt Cutajar, Michael Osborne, Maurizio Filippone, Stephen Roberts
The scalable calculation of matrix determinants has been a bottleneck to the widespread application of many machine learning methods such as determinantal point processes, Gaussian processes, generalised Markov random fields, graph models and many others.
no code implementations • 5 Apr 2017 • Jack Fitzsimons, Kurt Cutajar, Michael Osborne, Stephen Roberts, Maurizio Filippone
The log-determinant of a kernel matrix appears in a variety of machine learning problems, ranging from determinantal point processes and generalized Markov random fields, through to the training of Gaussian processes.
no code implementations • 20 Mar 2016 • Sid Ghoshal, Stephen Roberts
Financial markets are notoriously complex environments, presenting vast amounts of noisy, yet potentially informative data.
no code implementations • 27 Oct 2015 • Thomas Nickson, Tom Gunter, Chris Lloyd, Michael A. Osborne, Stephen Roberts
We present Blitzkriging, a new approach to fast inference for Gaussian processes, applicable to regression, optimisation and classification.
no code implementations • 24 Jul 2015 • Yves-Laurent Kom Samo, Stephen Roberts
In particular, we prove that some string GPs are Gaussian processes, which provides a complementary global perspective on our framework.
no code implementations • 2 Jul 2015 • Steven Reece, Roman Garnett, Michael Osborne, Stephen Roberts
This paper proposes a novel Gaussian process approach to fault removal in time-series data.
no code implementations • 7 Jun 2015 • Yves-Laurent Kom Samo, Stephen Roberts
In this paper we propose a family of tractable kernels that is dense in the family of bounded positive semi-definite functions (i. e. can approximate any bounded kernel with arbitrary precision).
no code implementations • 7 Jun 2015 • Yves-Laurent Kom Samo, Stephen Roberts
We introduce a new class of nonstationary kernels, which we derive as covariance functions of a novel family of stochastic processes we refer to as string Gaussian processes (string GPs).
no code implementations • 24 May 2015 • Timos Papadopoulos, Stephen Roberts, Kathy Willis
Biodiversity monitoring using audio recordings is achievable at a truly global scale via large-scale deployment of inexpensive, unattended recording stations or by large-scale crowdsourcing using recording and species recognition on mobile devices.
no code implementations • 24 Oct 2014 • Yves-Laurent Kom Samo, Stephen Roberts
In this paper we propose the first non-parametric Bayesian model using Gaussian Processes to make inference on Poisson Point Processes without resorting to gridding the domain or to introducing latent thinning points.
no code implementations • 18 Mar 2014 • Nabeel Gillani, Rebecca Eynon, Michael Osborne, Isis Hjorth, Stephen Roberts
Massive Open Online Courses (MOOCs) bring together thousands of people from different geographies and demographic backgrounds -- but to date, little is known about how they learn or communicate.
no code implementations • 17 Feb 2014 • Jan-Peter Calliess, Michael Osborne, Stephen Roberts
Existing work in multi-agent collision prediction and avoidance typically assumes discrete-time trajectories with Gaussian uncertainty or that are completely deterministic.
no code implementations • 23 Oct 2013 • Steven Reece, Stephen Roberts, Siddhartha Ghosh, Alex Rogers, Nicholas Jennings
We apply our approach to model the thermal dynamics of domestic buildings and show that it is effective at predicting day-ahead temperatures within the homes.