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.
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Showing 1 to 20 of 22 for “"ARIMA model"”.
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A natural language processing approach to improve demand forecasting in long supply chains
… processing (NLP) techniques in a deep learning model, known as NEMO, to forecast the demand of a commodity -- without requiring downstream companies to share information. In addition, this thesis compares the effectiveness of such an approach with other non-deep learning approaches, specifically …
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Forecasting short term trucking rates
… cash flows. This study develops a forecasting model that predicts both contract and spot rates for truckload transportation on individual lanes for the next seven days. This study considers several input variables, including lagged values of spot and contract rates, rates on adjacent routes and …
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Forecasting demand for district heating using different forecasting methods
… system. This thesis compares five different models for such forecasts. First, the Auto-Regressive Integrated Moving Average model, or ARIMA, predicted the general average usage based on previous data and was used as a benchmark for other models. Another regression model was created, LOWESS or …
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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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Application of Deep Neural Networks in Forecasting Foreign Currency Exchange rates
… the success of the Long Short-Term Memory (LSTM) model to the performance of AutoRegressive Integrated Moving Average (ARIMA) and Support vector regression (SVR) when predicting forex rates of US Dollar (USD) pair with South African Rand (ZAR) using daily timeframe data obtained from the …
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Modeling and prediction of wind power data
… power prediction. We used different time series models in statistics to predict wind power, including ARIMA model, SARIMA model, ARAR model, Holt-Winters method, and a state-space model. We compared the difference between the predicted data and the original data. We conclude that a state space …
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Predicting social unrest events in South Africa using LSTM neural networks
… the Autoregressive Integrated Moving Average (ARIMA model). The type of neural network implemented was the Long Short-Term Memory (LSTM) neural network. The basic theoretical concepts of ARIMA and LSTM neural networks are explained and subsequently, the patterns of the social unrest time series …
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Anomaly detection in semiconductor manufacturing through time series forecasting using neural networks
… The study is performed on three predictive models with increasing complexity for comparison. The three models are namely: Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP) and Long Short Term Memory (LSTM). ARIMA is a statistical model while MLP and LSTM are …
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Extreme weather disaster resilient port and waterway infrastructure for sustainable global supply chain
… The objectives of this research are: (1) modeling shipping demand and level of service, (2) developing Landsat-8 satellite imagery based methodology for mapping surface types and landuse, and (3) assessing the impact of coastal disasters and climate related sea level rise. The …
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Modeling And Dynamic Resource Allocation For High Definition And Mobile Video Streams
… a simple, general and accurate video source model: Simplified Seasonal ARIMA Model: SAM). SAM is capable of capturing the statistical characteristics of video traces with less than 5% difference from their calculated optimal models. SAM is shown to be capable of modeling video traces encoded …
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Shape based classification and functional forecast of traffic flow profiles
… that can result in better estimates of model parameters.</p><p>Lastly, a functional time series approach was proposed to forecast traffic flow for short and medium-term horizons. It is based on functional principal components decomposition to forecast three different traffic scenarios. …
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Using Machine Learning to Classify Danger Levels and Prediction Techniques to Forecast Temperature Changes with Height in Burning Sites
… for classifying the danger levels and prediction models to forecast temperature changes with height and time in burning sites using environmental factors such as temperature, smoke, and carbon monoxide, CO. The classifier algorithms bring the chief firefighter’s awareness of the danger levels in …
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Model selection-regression and time series applications
… In practice researcher will propose one model or a group of competing models that attempts to explain the data being investigated. This process is known as model selection. Model selection techniques have been developed to aid researchers in finding a suitable approximation to the true …
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The Development of Measurement and Characterization Techniques of Road Profiles
… characteristics of the road, a reduced set of models can be created from which appropriate representations of the terrain can be synthesized. Understanding the characteristics of the terrain requires the ability to accurately measure the terrain topology. It is only by increasing the fidelity …
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Effects of Parental Notification and Consent Laws on Teenage Births and Abortions in Texas
… Auto Regressive Integrated Moving Average (ARIMA) model-fitting processes, to identify any changes in the patterns of the dependent variables resulting from this legislation. Overall, parental notification and consent laws did seem to have an effect on birth and abortion rates for minors in …
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Flood risk assessment using multi-sensor remote sensing, geographic information system, 2D hydraulic and machine learning based models
… hazard and risk for predicting and modelling flooding events. However, this research proposes multiple state-of-the-art approaches to assess, simulate and forecast flooding from recent satellite imagery. Firstly, a model was proposed to monitor changes in surface runoff and forecast …
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My name is South Africa and I have a drinking a problem: a multicentre quasi-experimental analysis on alcohol regulation and injury presentations to emergency centres.
… an autoregressive integrated moving average (ARIMA) model with the level and slope of the model in the pre-intervention period being the counterfactual against the observed actual post-intervention level and slope. The primary outcome was the relative percent increase or decrease in the level …
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Microzooplankton Dynamics in Lower Chesapeake Bay and Its Major Tributaries
… density, as determined by time series analysis (ARIMA Model). However, for the smaller components (ciliates) in size, the autocorrelation function coefficients were not significant at most stations, which indicated their seasonal abundance patterns were not periodic.</p> <p>In terms of the …
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Geospatial Analysis of the Global Supply Chain and Transportation Infrastructure Considering Extreme Weather, Climate, and Sustainable Energy Policies
… rise. The next step in this research deals with modeling the Earth’s temperatures through autoregressive integrated moving average (ARIMA) model equations. ARIMA modeling allows for cyclical and seasonal time series data, such as climate indicators, to be modelled with accuracy where otherwise a …
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Analysis and prediction of traffic accidents at urban intersections
… trajectories derived from the microsimulation model are further examined using the surrogate safety assessment model to ascertain the distribution of time-to-conflict between vehicles. This analysis facilitates the estimation of risk for crash by employing extreme value theory. The findings …
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