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 20 of 38 for “"Autoregressive moving average"”.
-
On stationary and nonstationary fatigue load modeling using autoregressive moving average (ARMA) models
… by a class of time series referred to as Autoregressive Moving Average (ARMA) models, while a Fourier series is used to account for the variation of the mean and variance. Due to the use of random phase angles in the Fourier series, an ensemble of mean and variance variations is obtained. …
-
Exact Maximum Likelihood Estimation of the Kalman Filter Model
… of a regression equation to follow an autoregressive-moving average process. Thus the KFM is a generalization of the random coefficients model.
-
Electricity Price Forecasting Using a Convolutional Neural Network
… business strategies. Statistical analysis with autoregressive moving average methods and computational intelligence approaches using artificial neural networks dominate the landscape. With the rise in popularity of convolutional neural networks to handle problems with large numbers of inputs, …
-
NONUNIFORMLY AND RANDOMLY SAMPLED SYSTEMS
… patterns is developed. Parameter estimation of autoregressive moving average models using partial observations and an algorithm to fill in the missing data are proved and demonstrated by simulation programs. Interpolation of missing data using bandlimiting assumptions and discrete Fourier …
-
Comparison of linear parametric models for predicting fMRI response
… different linear parametric models including Autoregressive model (ARX), Autoregressive Moving Average Model (ARMAX), Box-Jenkins Model (BJ), Instrument Variable Model (IV) and Prediction Error Model (PEM) were used to predict the fMRI response and their performances compared. Transfer …
-
Calibrating high frequency trading data to agent based models using approximate Bayesian computation
… calibrate simple toy models, such as autoregressive moving average models, it fails to calibrate this agent based model for high frequency trading. This may be for two key reasons, either the parameters of the model are not uniquely identifiable given the model output or the SMC ABC …
-
Contemporaneous Carma Modelling With Applications
… statistical properties of the contemporaneous Autoregressive Moving-Average (CARMA) model. The research results constitute a more general framework than previously available for the analysis of many actual sets of time series data. It is shown in the thesis that the joint estimation is …
-
Empirical Bayes methods in time series analysis
… estimates of various time series parameters: The autoregressive model, moving average model, mixed autoregressive-moving average, regression with time series errors, regression with unobservable variables, serial correlation, multiple time series and spectral density function. In each case, …
-
Data Center Load Forecast Using Dependent Mixture Model
… proposed method proved better than the classical autoregressive, moving-average, as well as the neural network-based forecasting method, and resulted in a reduction of 7.91% mean absolute percentage error (MAPE) for the forecast. A more accurate forecast can improve power scheduling and resource …
-
Dynamic Chemical Shift Imaging For Image-Guided Thermal Therapy
… spatiotemporal resolution. An algorithm based on autoregressive moving average (ARMA) modeling is developed and validated to help overcome limitations of Fourier-based analysis allowing highly accurate and precise PRF estimates. From the determined acquisition parameters and ARMA modeling, robust …
-
Linear Modeling and Prediction in Diabetes Physiology
… 9 T1DM patients data. Model structures include: autoregressive moving average with exogenous inputs (ARMAX) models and state-space models.ARMAX multi-step-ahead predictors were estimated by means of least-squares estimation; next regularization of the autoregressive coefficients was introduced. …
-
Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability
… modeled and gauge data using: 1) Wavelets 2) Autoregressive-moving-average (ARMA) model / Empirical Mode Decomposition (EMD) 3) Vector Generalized Linear Model (VGLM). This dissertation aims to propose and evaluate a methodology for developing a model to measure ENSO events accurately. The …
-
A modified approach for obtaining sieve bootstrap prediction intervals for time series
… but many early versions of such intervals for autoregressive moving average (ARMA) processes assume that the autoregressive and moving average orders, p, q respectively, are known, The sieve bootstrap, first introduced by Bühlmann in 1997, sidesteps this assumption for invertible time series by …
-
Tracking autonomic balance using open and closed loop models of the arterial baroreflex
… transfer functions, or parametrically as an autoregressive moving average model. In this work, we formulate beat-to-beat, open and closed loop models of the heart rate arc of the baroreflex. Model structure and parameterization are explicitly based on prior physiological insights of the …
-
Effect of manual digitizing error on the accuracy and precision of polygon area and line length
… Stream mode digitizing error was modeled using autoregressive moving average (ARMA) procedures, and point mode digitizing was stochastically simulated using an uniform random model. These models were developed based on quantification of digitizing error collected from several operators. The …
-
Multiresolution fixation of a binocular vision system
… determines an initial window size using an Autoregressive-Moving Average (ARMA) modeling technique. Area-based feature matching is performed using normalized cross-covariance within a multiresolution image hierarchy. Gaussian low-pass filters of increasing spatial resolution are used to …
-
The analysis of some bivariate astronomical time series
… is investigated, and appropriate univariate autoregressive moving average models given. The results of extensive transfer function fitting using respectively the λ1337 and λ1350 continuum variations as input series, are presented. There is little evidence for a dead time in the response of …
-
Forecasting Wind Direction Across Very Short and Short Term Time Horizons for Wind Turbine Control
… wind direction forecasting: persistence, autoregressive moving average (ARMA), and ridge regression. The models were tested on several time horizons (Δ𝑇) that are relevant to turbine control and operation, ranging from 30 seconds to 2 hours. In this thesis, persistence demonstrated the …
-
Forecasting of work in process quality using Holt-Winters method for missing observations
… were generated using different processes such as Autoregressive and Autoregressive Moving Average process. Some of the values in each data set were assumed to be missing. The factors that were considered for forecasting using this method were the level, trend and the seasonal factor. Forecasting …
-
Essays on unit root testing in time series
… when the time series can be modeled using an autoregressive moving average (ARMA) process, such tests aim to determine if the autoregressive (AR) polynomial has one or more unit roots. The effect of economic shocks do not diminish with time when there is one or more unit roots in the AR …
Page 1 of 2