Searched for: subject%3A%22Time%255C+series%255C+forecasting%22
(1 - 15 of 15)
document
Shankar, Aditya (author)
Vertical federated learning’s (VFL) immense potential for time series forecasting in industrial applications such as predictive maintenance and machine control remains untapped. Critical challenges to be addressed in the manufacturing industry include small and noisy datasets, model explainability, and stringent privacy requirements for training...
master thesis 2023
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Kielhöfer, Marius (author)
The ability to accurately forecast sales volumes holds substantial significance for businesses. Current classical models struggle in capturing the impact of different variables upon the sales volume. These machine learning models are also not applicable to more than one specific product. The Temporal Fusion Transformer (TFT) is implemented to...
bachelor thesis 2023
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Üzel, Ziyar (author)
Accurate short-term traffic forecasting plays a crucial role in Intelligent Transportation Systems for effective traffic management and planning. In this study, the performances of three popular forecasting models are explored: Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Facebook's Prophet, for short-term...
bachelor thesis 2023
document
Kienhuis, Maurits (author)
The ever-evolving power grid is becoming smarter and smarter. Modern houses come with smart meters and energy conscious consumers will buy additional smart meters to place in their home to help monitor their energy consumption. This new smart technology also opens the door to more accurate power consumption forecasting. In this study we look at...
bachelor thesis 2023
document
Mao, Kangmin (author)
Modeling the relationship between rainfall and runoff is a longstanding challenge in hydrology and is crucial for informed water management decisions. Recently, Deep Learning models, particularly Long short-term memory (LSTM), have shown promising results in simulating this relationship. The Transformer, a newly proposed deep learning...
master thesis 2023
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de Boer, Jens (author)
A significant decrease in greenhouse gas emissions can be achieved by including a prediction of future power consumption in the control of ships’ power plants during transits. Moreover, including a prediction of power consumption in the Energy Management System (EMS) during Dynamic Positioning (DP) operations can also contribute to a reduction...
master thesis 2023
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Hoogeveen, Sylle (author)
The increase in complexity of mathematical models in an attempt to approximate reality and desire to have near real-time results have emphasized the need for fast numerical simulations. Especially in areas where classic numerical methods struggle to produce valid solutions in reasonable computational time due to their<br/>complex behaviour on...
master thesis 2022
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Bos, Thomas (author)
Explainable artificial intelligence has in recent years allowed us to investigate how many machine learning methods are creating its predictions. This is especially useful in scenarios where the goal is not to predict a variable, but to explain what influences that variable. However, the methods that have been created thus far do not focus on...
master thesis 2022
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van 't Wout, Maarten (author)
Handling missing values is crucial for accurately forecasting time series with different sampling rates. In stock price prediction, for example, the daily stock prices and quarterly valuation figures are sampled at a different rate, and both are useful in estimating the daily stock price’s future. This research proposes combining imputation...
master thesis 2021
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Calkoen, Floris (author), Luijendijk, Arjen (author), Rivero, Cristian Rodriguez (author), Kras, Etienne (author), Baart, F. (author)
Forecasting shoreline evolution for sandy coasts is important for sustainable coastal management, given the present-day increasing anthropogenic pressures and a changing future climate. Here, we evaluate eight different time-series forecasting methods for predicting future shorelines derived from historic satellite-derived shorelines....
journal article 2021
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Verbruggen, Roderik (author)
Research and Objective: In the recent years the online grocery sector experienced an enormous uplift and evolved to a highly competitive business sector. Within this demanding environment, the need for strategic information has become extremely important, as it greatly enhances decision-making processes and the optimisation of the supply chain....
master thesis 2020
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Claes, Jochem (author)
The Low Earth Orbit (LEO) region has been attractive to many space agencies and organisations because of its ease of access and the ideal opportunity for remote sensing. Due to the low altitudes, a satellite's orbital state is highly affected by the atmospheric drag force acting on the satellite's body. The largest variation in this drag force...
master thesis 2019
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Hoogendoorn, Jasper (author)
In this thesis, we study the sequential Monte Carlo method for training neural networks in the context of time series forecasting. Sequential Monte Carlo can be particularly useful in problems in which the data is sequential, noisy and non-stationary. We compare this algorithm against a gradient-based method known as stochastic gradient descent ...
master thesis 2019
document
Khuntia, S.R. (author), Rueda, José L. (author), van der Meijden, M.A.M.M. (author)
Long-term electricity load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment on the construction of excess power facilities, while an underestimate of the future load will result in...
journal article 2018
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Yan, Gaowei (author), Jia, Songda (author), Ding, Jie (author), Xu, Xinying (author), Pang, Y. (author)
In this paper, a local cloud model similarity measurement (CMSM) is proposed as a novel method to measure the similarity of time series. Time series similarity measurement is an indispensable part for improving the efficiency and accuracy of prediction. The randomness and uncertainty of series data are critical problems in the processing of...
journal article 2018
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