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Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 76 for “"Time series forecasting"”.
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Time series forecasting with recurrent neural networks
Time series, such as demand trends, stock prices, and sensor data, is an essential data type in our modern world. Over the years, many models such as Exponential Smoothing and ARIMA are developed to make forecasts on time series. Recently, Recurrent Neural Networks (RNN) is gaining traction in the …
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Coherency Loss for Hierarchical Time Series Forecasting
In hierarchical time series forecasting, some series are aggregated from others, producing a known coherency metric between series. We present a new method for enforcing coherency on hierarchical time series forecasts. We propose a new loss function, called Network Coherency Loss, that minimizes …
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Graph-based Time-series Forecasting in Deep Learning
Time-series forecasting has long been studied and remains an important research task. In scenarios where multiple time series need to be forecast, approaches that exploit the mutual impact between time series results in more accurate forecasts. This has been demonstrated in various applications, …
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Deep Learning-based Time Series Forecasting: Models and Applications
… As a typical representative of complex data, time series modeling and forecasting have always been hot topics. In the big data environment, time series often have the characteristics of multi-source complexity, dynamic heterogeneity, uncertainty, and nonlinearity, which brings tremendous …
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Time Series Forecasting Modeling for Demand of Emergency Department
"본 연구는 응급의료센터 수요예측의 주요기준이 되는 ‘응급의료센터 일일 내원 환자 수’를 예측하는 시계열 모델을 개발하는 것이 목적이다. ‘응급의료센터 일일 내원 환자수’를 미리 예측함으로써 수요에 맞게 응급의료 센터의 한정된 자원을 효율적으로 배분하고 시기 적절하게 활용할 수 있을 뿐 아니라 응급의료센터에 내원하는 환자들의 만족도나 치료 결과, 예후에도 좋은 영향력을 미칠 수 있을 것이다. 본 연구에 사용된 데이터는 병원정보시스템 데이터베이스로부터 응급의료센터 내원 환자 정보를 수집하였으며, 예측모델 개발을 …
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Essays in Hierarchical Time Series Forecasting and Forecast Combination
… of three original contributions to empirical forecasting research. Chapter 1 introduces the dissertation. Chapter 2 contributes to the literature on hierarchical time series (HTS) modelling by proposing a disaggregated forecasting system for both inflation rate and its volatility. Using …
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Applications of Deep Learning to Financial Time Series Forecasting
… architecture performs at the task of volatility forecasting by comparing its performance against that of previously explored deep learning architectures such as the LSTM.
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Error magnitude and directional accuracy for time series forecasting evaluation
… measurement in accordance to the purpose of forecasting. Commonly, accuracy is measured in terms of error magnitude. However, directional accuracy is as important as error magnitude especially in economics since it considers directional movement of the data. This research attempted to combine …
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Recurrent error-based ridge polynomial neural networks for time series forecasting
Time series forecasting has attracted much attention due to its impact on many practical applications. Neural networks (NNs) have been attracting widespread interest as a promising tool for time series forecasting. The majority of NNs employ only autoregressive (AR) inputs (i.e., lagged time series …
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Short-Term Wind Speed Time Series Forecasting Using Artificial Neural Networks
… neural networks require less training time and often give reasonable results; while the ConvLSTM networks need longer time to train and implement, but it may provide a slightly better accuracy in some cases.</p>
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Design and Applications of Machine Learning Algorithms for Time-Series Forecasting
… machine learning is concerned with analyzing and forecasting time series datasets. Time series forecasting models help to predict future values of events having significant impact on our life, such as timely healthcare services and prediction of health conditions of a patient, forecasting the …
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Anomaly detection in semiconductor manufacturing through time series forecasting using neural networks
… study into anomaly detection through time series forecasting is carried out with application to a plasma etch case study. The study is performed on three predictive models with increasing complexity for comparison. The three models are namely: Autoregressive Integrated Moving Average …
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IMPROVING MULTI-VARIATE TIME SERIES FORECASTING WITH DYNAMIC MULTI-HEAD ATTENTION ADJACENCY MATRIX
Time series data is prevalent in many fields, such as finance, weather forecasting, and economics. Predicting future values of a time series can offer valuable insights for decision-making, such as identifying trends, detecting anomalies, and improving resource allocation. Existing research, …
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Empirical Analysis of Neural Architectures and Side Information in Financial Time Series Forecasting
… capabilities of neural networks in financial time series forecasting, focusing on predicting the weekly close price of the SPY index. We explore the integration of options-derived features alongside traditional price data, compare recurrent architectures and transformer-based models, and …
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Detecting macroeconomic impacts on agricultural prices and export sales: a time series forecasting approach
… principle of Granger causality. An out-of-sample forecasting procedure is used to conduct tests for Granger causality from the exchange rate to agricultural prices and export sales. Technical time series issues such as stationarity, the method of lag-length selection, in sample versus …
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Towards Energy-Efficient Cloud Datacentres: A Unified Framework for Generative and Multi-Scale Time Series Forecasting
… achieving an optimised resource provisioning. Time-series forecasting driven resource optimisation is a potential strategy to workload volatility and sustaining operational efficiency under an increasingly heterogeneous workload behavioural profile. This thesis addresses these challenges by …
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