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 19 of 19 for “"Autoregressive Integrated Moving Average (ARIMA)"”.
-
Time series analysis of RTC Great Lakes recruit graduate data
… to produce a model. As an alternative the autoregressive integrated moving average (ARIMA) process is used to describe the data. In both instances, satisfactory forecasting results are attained.
-
Time Series Analysis of Stock Prices Using the Box-Jenkins Approach
… of the data and forecast future values. The Autoregressive Integrated Moving Average (ARIMA) models, or Box-Jenkins methodology, are a class of linear models that are capable of representing stationary as well as nonstationary time series. ARIMA models rely heavily on autocorrelation …
-
Application of Deep Neural Networks in Forecasting Foreign Currency Exchange rates
… 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 Metatrader trading platform. The LSTM …
-
Monitoring the Process Mean of Autocorrelated Data
… family of models for time series data are the autoregressive integrated moving average (ARIMA) models. These models are well suited to model production processes, in which the observations are autocorrelated. It is our interest to examine these models. Meaning is given to the process being …
-
Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting
… statistical methods, including regression, autoregressive integrated moving average (ARIMA), and generalised additive models, were compared alongside machine learning methods including extreme gradient boosting, support vector machines, and long short-term memory networks. Extreme gradient …
-
Predicting social unrest events in South Africa using LSTM neural networks
… traditional forecast method selected being 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 …
-
Anomaly detection in semiconductor manufacturing through time series forecasting using neural networks
… 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 neural network models. The results from the control experiment, under supervised …
-
Forecasting short term trucking rates
… is a neural network based on Nonlinear Autoregressive Models with eXogenous input (NARX) models. NARX models are powerful when modelling complex, nonlinear and dynamic systems, especially time series. Traditional time series models, including autoregressive integrated moving average …
-
Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods
… different time series analysis techniques: 1) autoregressive integrated moving average (ARIMA), and 2) singular spectrum analysis (SSA). Approximately 40% of the S&P 500 stocks are analyzed. Forecasts are generated for one and five days ahead using daily closing prices. Univariate and …
-
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.
… design was a quasi-experimental ITS, we used 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 …
-
The design of a real-time nowcasting system for localised weather
… of constructing stochastic models based on the AutoRegressive Integrated Moving Average (ARIMA) technique. The satellite images contain features (such as cloud formations) which evolve dynamically and may be subject to movement, growth, distortion, bifurcation, superposition, or elimination …
-
Demonstrating Long-Term Primary Care Need For Medically Vulnerable Populations Post-Disaster
… an interrupted time-series analysis by fitting autoregressive integrated moving average (ARIMA) models to 1) determine the impact of an EPHD on health care access among directly affected Medicaid beneficiaries and 2) forecast monthly patient volume of ACSC in medically vulnerable population …
-
Modeling and Forecasting Ghana's Inflation Rate Under Threshold Models
… rate in Ghana using linear models such as Autoregressive Integrated Moving Average (ARIMA), Autoregressive Moving Average (ARMA) and Moving Average (MA). Empirical research however, has shown that financial data, such as inflation rate, does not follow linear patterns. This study seeks to …
-
Geospatial Analysis of the Global Supply Chain and Transportation Infrastructure Considering Extreme Weather, Climate, and Sustainable Energy Policies
… 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 linear trend model would not do so. The …
-
Sieve bootstrap based prediction intervals and unit root tests for time series
… series, to obtaining prediction intervals for integrated, long-memory, and seasonal time series as well as constructing a test for seasonal unit roots, is considered. The advantage of this resampling method is that it does not require knowledge about the underlying process generating a given …
-
Flood risk assessment using multi-sensor remote sensing, geographic information system, 2D hydraulic and machine learning based models
… used to detect the LULC changes. Moreover, an autoregressive integrated moving average (ARIMA) model was applied to analyse and forecast rainfall trends. The parameters of the ARIMA time series model were calibrated and fitted statistically to minimise prediction uncertainty through modern …
-
Extreme weather disaster resilient port and waterway infrastructure for sustainable global supply chain
… and climate related sea level rise. The Autoregressive Integrated Moving Average (ARIMA) model equations, the Artificial Neural Networks (ANN) models, and regression equations were developed using historical containerized cargo volumes to predict the future volumes for the Port of New …
-
Time series analysis for evaluation of antimicrobial stewardship interventions
… methods were segmented regression (70%) and Autoregressive Integrated Moving Average (ARIMA) (12%). There is no published consensus reporting guideline for interrupted time series studies but approximately half of the studies that used segmented regression or ARIMA also reported the results …
-
Analyzing the temperature dimension of University of Illinois electricity demand
Made available in DSpace on 2016-05-04T21:49:06Z (GMT). No. of bitstreams: 2 GUERRERO-THESIS-2015.pdf: 2270398 bytes, checksum: 440c53b436df1200cd5e84da507ec2d7 (MD5) LICENSE.txt: 4213 bytes, checksum: fa95ff9e926f13b985b5f5e38e2af56e (MD5) Previous issue date: 2015-07-22