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 21 for “"ARIMA models"”.
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An evaluation of univariate time-series models of quarterly earnings per share and their generalization to models with autoregressive conditionally heteroscedastic disturbances
This study evaluates time-series models of quarterly earnings per share (EPS) in order to determine whether there are any changes in the residual variance to be modeled by the GARCH procedure. The results of statistical analyses indicate the presence of GARCH effect in the residuals generated from …
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Time Series Analysis of Stock Prices Using the Box-Jenkins Approach
… 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 patterns. This paper will explore the application …
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Time series analysis of macroeconometric constructs
… persistence of economic shocks using time series models. First, it is shown that the log likelihood function for ARIMA models is not strictly quadratic with respect to the persistence estimate. This result explains why the persistence literature has attained conflicting results. In addition, …
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Constructing an Informative Prior Distribution of Noises in Seasonal Adjustment
… this area, and one of the latest methods is X-13ARIMA-SEATS, which is built on ARIMA models and linear lters. On the other hand, state space modelling (abbreviated to SSM) is also a popular method to solve this problem and researchers including J. Durbin, S.J. Koopman and and A. Harvery have …
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Forecasting volatilities in option pricing: An application of Bayesian inference
… forecasting volatilities used in option pricing models for live cattle and live hog futures. The forecast problem is cast in the framework of Bayesian inference. Six types of individual forecast models are used--GARCH models, ARIMA models, systems of simultaneous equations, systems of seemingly …
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Application of Deep Neural Networks in Forecasting Foreign Currency Exchange rates
… 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 outperformed the SVR and ARIMA models …
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Monitoring the Process Mean of Autocorrelated Data
… control charting procedures. One 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. …
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Call center demand forecasting : improving sales calls prediction accuracy through the combination of statistical methods and judgmental forecast
… sales call volume based on the combination of ARIMA models and judgmental forecasting. The proposed methodology improves the accuracy of weekly forecasted call volume from 23% to 46% and of daily volume from 27% to 41%. Further improvements are easily achievable through the adjustment and …
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Does Google Search Data Aid In Predicting Unemployment?
… This paper looks to improve upon existing models by adding Google search data to traditional models using initial claims or replacing initial claims with Google searches. One hypothesis is that Google searches may improve forecast accuracy due to employees knowing or getting a sense when …
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Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting
… of the influencing factors. Many existing models and projects focus on long-term trends in coastal water levels particularly in terms of climate change and global warming. This project investigated the application of time series analysis with exogenous meteorological variables to the task …
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AN ECONOMETRIC ANALYSIS OF US FARMLAND PRICES, 1941 TO 1980 (UNITED STATES)
… including: (1) the lack of stability of models; (2) the exclusion of relevant variables; (3) the use of single equation estimation techniques; and (4) the inability to accurately forecast current farmland price movements. The purpose of this study was to overcome the problems of earlier …
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Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models
… and multivariate time series predictive models as applied to Bitcoin, other cryptocurrencies, and other related financial time series data. ARIMA models, long regarded as the gold standard of univariate financial time series prediction due to both its flexibility and simplicity, are used …
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Value chain diversification in the sugar industry using quantitative economic forecasting models
… for model building. Seven different types of models were considered, including the Naïve method, simple and weighted moving averages, simple exponential smoothing, Holt's method, Holt-Winters' method and Auto-Regressive Integrated Moving Average (ARIMA) models. Each type of model was analysed …
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Regionally dissected temperature and rainfall models for the South Island of New Zealand
… Northern (Otago) and Southern zone for analysis. ARIMA and regression models have been developed to allow an estimation of longer term temperature and rainfall variability on a regional basis. The study identified regional differences in current and ARIMA simulated rainfall and temperature trends. …
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Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods
… 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 multivariate structures are applied and results are …
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Exponential Smoothing for Forecasting and Bayesian Validation of Computer Models
… We investigate three types of statistical models that have been found to underpin ES methods. They are ARIMA models, state space models with multiple sources of error (MSOE), and state space models with a single source of error (SSOE). We establish the relationship among the three classes …
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Análisis estadístico comparativo de series cronológicas de parámetros de calidad del agua: valoración de diferentes modelos de predicción
… DE ANALISIS DE SERIES TEMPORALES DE MODELOS ARIMA EN EL TRATAMIENTO DE UN EXTENSO GRUPO DE MEDIDAS DE PARAMETROS DE CALIDAD DEL AGUA DE LA CUENCA DEL RIO GUADIANA. LA ELECCION DE LA METODOLOGIA BOX-JENKINS PARA DICHO ANALISIS HA SIDO CONSECUENCIA DE UNA AMPLIA REVISION Y SUBSIGUIENTE …
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Demonstrating Long-Term Primary Care Need For Medically Vulnerable Populations Post-Disaster
… 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 recovering from an EPHD. 3) Perform …
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Essays on international financial markets interdependence
… of both (in-sample and out-of-sample) models in predicting equity returns. Thus, using daily data, this chapter examines whether the U.S. S&P stock exchange follow a random walk process, which required by market efficiency. We use a model-comparison approach, which compares an ex-post …
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Análise e previsão de séries temporais com modelos ARIMA e análise espectral singular
… são focados em particular os modelos do tipo ARIMA e a Análise Espectral Singular, como ferramentas de trabalho na análise e previsão de séries temporais. Pretendeu-se contribuir um pouco para melhorar a abordagem da análise e previsão das séries temporais, ilustrando com exemplos e recurso ao …
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