Causal Discovery from Temporal Data: An Overview and New Perspectives

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tasks, eg, classification, clustering and prediction, have been proposed in the past decades. Among them, causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task and has attracted much research attention. Existing casual discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series casual discovery, and event sequence casual discovery. However, most previous surveys are only focused on the time series casual discovery and ignore the second category. In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data casual discovery.Comment: 52 pages, 6 figure

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

On Price Responsive Consumer Behavior in Electricity Markets: to Machina Economicus from Homo Agens

The electricity power market is well known for its highly volatile nature due to its innate variability characteristic of demand and the absence of practical bulk storage at reasonable cost. Any discordance between rapid fluctuation in wholesale prices and near flat retail prices not only incurs economic inefficiency in terms of social welfare, but also creates price-inelastic wholesale demand which severely exacerbates the volatility of wholesale electricity prices. While the market has a fundamental dynamic nature, the behavioral aspect of power consumption in response to price changes is not well understood. This necessitate to develop a empirical modeling methodology of demand which can potentially provide practical insights into demand response. In the former part of this work, we focus on dynamic aspect of demand response in Chapter 2. We first show that (i) demand is well responsive to outlier high price surges, and (ii) demand response can incur a certain amount of delay. Examining further data, it appears that demand is responsive to anticipated prices. This is in conformity with our previous observations on the inertia of demand, and testing the hypothesis that demand actually responds to anticipated prices rather than actual real time prices is an important next step. While it is impractical to obtain a particular individual’s own price prediction, We propose to test the hypothesis with day-ahead electricity prices (DAP). In addition, as an initial step toward the derivation of a quantitative model of electricity load and price, we propose a model of “appliance” usage as a representative basic component of electricity load. In the latter part of this work, we investigate more fundamental aspect of data-centric modeling in Chapter 3. First, we show the limitation of pure data-centric modeling strategy by proving that having a perfect knowledge on the joint distribution on price and load does not identify the load behavior in response to price. As it turns out that the causal structure of the variables of interest is the central matter that determines load behavior identifiability, we derive a minimal identifiable causal structure of demand response from the preexisting economic theories. Based on the discovered causal structure, we propose a minimal Bayesian model representation called “stochastic neuron” which connects machine learning technique to demand response modeling. We show that a stochastic neuron is an explainable tool as expressive as an ordinary neural network, and well extends the arguments from “appliance” usage model

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Novel methods for biological network inference: an application to circadian Ca2+ signaling network

Biological processes involve complex biochemical interactions among a large number of species like cells, RNA, proteins and metabolites. Learning these interactions is essential to interfering artificially with biological processes in order to, for example, improve crop yield, develop new therapies, and predict new cell or organism behaviors to genetic or environmental perturbations. For a biological process, two pieces of information are of most interest. For a particular species, the first step is to learn which other species are regulating it. This reveals topology and causality. The second step involves learning the precise mechanisms of how this regulation occurs. This step reveals the dynamics of the system. Applying this process to all species leads to the complete dynamical network. Systems biology is making considerable efforts to learn biological networks at low experimental costs. The main goal of this thesis is to develop advanced methods to build models for biological networks, taking the circadian system of Arabidopsis thaliana as a case study. A variety of network inference approaches have been proposed in the literature to study dynamic biological networks. However, many successful methods either require prior knowledge of the system or focus more on topology. This thesis presents novel methods that identify both network topology and dynamics, and do not depend on prior knowledge. Hence, the proposed methods are applicable to general biological networks. These methods are initially developed for linear systems, and, at the cost of higher computational complexity, can also be applied to nonlinear systems. Overall, we propose four methods with increasing computational complexity: one-to-one, combined group and element sparse Bayesian learning (GESBL), the kernel method and reversible jump Markov chain Monte Carlo method (RJMCMC). All methods are tested with challenging dynamical network simulations (including feedback, random networks, different levels of noise and number of samples), and realistic models of circadian system of Arabidopsis thaliana. These simulations show that, while the one-to-one method scales to the whole genome, the kernel method and RJMCMC method are superior for smaller networks. They are robust to tuning variables and able to provide stable performance. The simulations also imply the advantage of GESBL and RJMCMC over the state-of-the-art method. We envision that the estimated models can benefit a wide range of research. For example, they can locate biological compounds responsible for human disease through mathematical analysis and help predict the effectiveness of new treatments

Bayesian Non-linear System Identification and Frequency Response Analysis with Application to Soft Smart Actuators

Newly emerging classes of next generation soft-smart actuators are set to have a huge impact on the fields of robotics, orthotics and prosthetics due to their lightweight, high-strain and muscle-like properties. Like muscle, these actuators can be used in multiple roles, e.g. both as actuators and brakes, due their variable compliance. One important class of soft actuator is the dielectric elastomer actuator (DEA). However, DEAs are extremely difficult to control due to their non-linear and time varying dynamics. A crucial step in the advancement of this technology is the development of techniques for systems level modelling and analysis, which is the focus of this thesis. In the first part of the thesis, a set of DEAs are identified and analysed using standard methods from the field of system identification, obtaining non-linear autoregressive with exogenous input (NARX) models. These provide a benchmark against which later methods are evaluated. The key novelty in this part is the development of NARX models of DEAs for use in non-linear frequency-domain analysis. This result provides insight for the first time into how a set of similarly fabricated DEAs vary in different ways. A further aspect of DEA behaviour is their unexplained time varying behaviour. The system identification approach used to identify NARX models of DEAs is in a convenient form such that it can be easily extended to cater for this time varying behaviour. There are however very few available methods for the frequency domain analysis of time varying systems. A novel method for time varying frequency domain analysis of NARX systems is developed in this work and applied to the DEAs. The analysis procedure is used to provide insight on how the dynamic behaviour of DEAs change over time. In the second part of the thesis a novel approach to the joint structure detection and parameter estimation of NARX models is developed using a sparse Bayesian method. The Bayesian framework allows for the estimation of posterior distributions over model parameters, characterising the model uncertainty. Analytic solutions are found that describe model uncertainty in the frequency-domain as confidence bounds on both linear and higher order frequency response functions. The sparse Bayesian identification algorithm is applied to the DEA data sets and is used to give the first non-linear dynamic model of DEAs with uncertainty bounds plus the first description of DEA dynamics in the frequency-domain, again with uncertainty bounds

Redefine time series models for transportation planning use

Time series models are used to model, simulate, and forecast the behaviour of a phenomenon over time based on data recorded over consistent intervals. The digital era has resulted in data being captured and archived in unprecedented amounts, such that vast amounts of information are available for analysis. Feature-rich time-series datasets are one of the data sets that have become available due to the expanding trend of data collection technologies worldwide. With the application of time series analysis to support financial and managerial decision-making, the development and advancement of time series models in the transportation domain are unavoidable. As a result, this thesis redefines time series models for transportation planning use with the following three aims: (1) To combine parametric and bootstrapping techniques within time series models; (2) to develop a time series model capable of modelling both temporal and spatial dependencies in time-series data; and (3) to leverage the hierarchical Bayesian modelling paradigm to accommodate flexible representations of heterogeneity in data. The first main chapter introduces an ensemble of ARIMA models. It compares its performance against conventional ARIMA (a parametric method) and LSTM models (a non-parametric method) for short-term traffic volume prediction. The second main chapter introduces a copula time series model that describes correlations between variables through time and space. Temporal correlations are modelled by an ARMA-GARCH model which enables a modeller to describe heteroscedastic data. The copula model has a flexible correlation structure and is used to model spatial correlations with the ability to model nonlinear, tailed and asymmetric correlations. The third main chapter provides a Bayesian modelling framework to raise awareness about using hierarchical Bayesian approaches for transport time series data. In addition, this chapter presents a Bayesian copula model. The combination of the two models provides a fully Bayesian approach to modelling both temporal and spatial correlations. Compared with frequentist models, the proposed modelling structures can incorporate prior knowledge. In the fourth main chapter, the fully Bayesian model is used to investigate mobility patterns before, during and after the COVID-19 pandemic using social media data. A more focused analysis is conducted on the mobility patterns of Twitter users from different zones and land use types