Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 15 of 15 for “"time series modelling"”.
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Time series modelling and inference with Bayesian Context Trees
Time series arise all over the sciences and engineering, with numerous important applications in many different fields. In the ‘Big Data Era’, the tasks of time series modelling, inference and prediction have become more critical than ever. In this thesis, we introduce a collection of statistical …
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Structural time series modelling for 18 years of Kapenta fishing in Lake Kariba
Includes abstract. Includes bibliographical references.
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Time series modelling of groundwater levels in a selected semi-arid catchment within Vhembe District Municipality, South Africa
This study is aimed at modelling groundwater levels in a semi-arid catchment within Vhembe District Municipality, South Africa. Auto Regressive Integrated Moving Average (ARIMA) model and Seasonal Auto Regressive Integrated Moving Average with eXogenous variables (SARIMAX) model were used to model …
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The use of hourly, rather than averaged, data time series modelling to improve projections of power usage and demand in California through to 2030
This thesis presents a comprehensive life-cycle and net energy analysis of California’s energy transition, focused on two major developments: the large-scale deployment of photovoltaic (PV) systems with lithium-ion battery (LIB) storage and the widespread adoption of battery electric vehicles …
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Bayesian networks for spatio-temporal integrated catchment assessment
… to facilitate data pre-processing and spatial modelling. Dynamic Bayesian Networks were implemented in the software for time-series modelling.
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Hidden state models for time series
Amongst all the objectives in the study of time series, uncovering the dynamic law of its generation is probably the most important. When the underlying dynamics are not available, time series modelling consists of developing a model which best explains a sequence of observations. In this thesis, …
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Essays in econometrics
… techniques to group observations from a series of repeated cross-sections to create a pseudo-panel of group averages. This clustering method is based on features of the data space and does not require external grouping variables unlike many other methods. Using a model of enterprise …
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Data mining, fraud detection and mobile telecommunications: call pattern analysis with unsupervised neural networks
… which define usage patterns. Over a period of time, an individual phone generates a large pattern of use. While call data are recorded for subscribers for billing purposes, we are making no prior assumptions about the data indicative of fraudulent call patterns, i.e. the calls made for billing …
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Information and generative deep learning with applications to medical time-series
Physiological time-series data are a valuable but under-utilised resource in intensive care medicine. These data are highly-structured and contain a wealth of information about the patient state, but can be very high-dimensional and difficult to interpret. Understanding temporal relationships …
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SOCIAL MEDIA IN POST-SOVIET SPACE: SERVING TO AUTOCRACY OR SIMULATING DISSENT?
… techniques: automated text analysis, time-series modelling, quasi-experimental methods and regression analysis. The empirical part of my studies concentrates on countries of the post-soviet region: Belarus, Lithuania, Russia and Ukraine. I argue that autocratic powers tend to be more …
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From big data to personal narratives: a supervised learning framework for decoding the course of traumatic brain injury in intensive care
… is never analysed or interpreted. At the same time, the dynamic, complex disease course of TBI is not sufficiently characterised for truly patient-tailored treatment. This thesis capitalises on an opportunity to widen the context of information considered by individualised, dynamic models of …
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Current and future consequences of tropospheric ozone on soybean biochemistry, physiology and yield
… yield responses on a larger scale, using time series modeling to determine the O3 response of soybean and maize. Time series models are commonly used to predict the potential effects of climate change on crop yields on a large scale, in an agronomic setting, over many years and growing …
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Toward effective and generalisable machine learning for biosignal time series
Biosignal time series collected from wearable devices, such as electrocardiograms (ECG), electroencephalograms (EEG), and Inertial Measurement Units (IMUs), enable continuous monitoring of human physiology and behaviour. Using these signals provides unique opportunities to advance personalised …
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SARIMA Short to Medium-Term Forecasting and Stochastic Simulation of Streamflow, Water Levels and Sediments Time Series from the HYDAT Database
… Integrated Moving Average (SARIMA) time series models. The methodology can account for linear trends in the time series that may result from climate and environmental changes. A Universal Canadian forecast Application using python web interface was developed to generate short-term …
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Bayesian Approaches to Tracking, Sensor Fusion and Intent Prediction
… Monte Carlo schemes are considered for the first time to tackle the sequential batch inference problems due to the presence of infrequent position data. Performance evaluation on both synthetic and real-world data shows that the proposed algorithms are superior to simpler particle filters, …