Digital Commons Network^™

A Change-Point Analysis Of Air Pollution Levels In Silao, Mexico And Fresno, California, Rachael Goodwin

WWU Honors College Senior Projects

We analyzed PM10 levels in the city of Silao, Mexico, as well as PM2.5 and PM10 levels in Fresno, California to determine if there was a shift in air pollution levels in either location. A change point based analysis was used to determine if there was a shift in air pollution levels. In the city of Silao, there was a significant increase in PM10 levels, but there was no significant change in Fresno for either pollutant.

Data Visualization, Dimensionality Reduction, And Data Alignment Via Manifold Learning, Andrés Felipe Duque Correa

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The high dimensionality of modern data introduces significant challenges in descriptive and exploratory data analysis. These challenges gave rise to extensive work on dimensionality reduction and manifold learning aiming to provide low dimensional representations that preserve or uncover intrinsic patterns and structures in the data. In this thesis, we expand the current literature in manifold learning developing two methods called DIG (Dynamical Information Geometry) and GRAE (Geometry Regularized Autoencoders). DIG is a method capable of finding low-dimensional representations of high-frequency multivariate time series data, especially suited for visualization. GRAE is a general framework which splices the well-established machinery from kernel …

Lstm-Sdm: An Integrated Framework Of Lstm Implementation For Sequential Data Modeling[Formula Presented], Hum Nath Bhandari, Binod Rimal, Nawa Raj Pokhrel, Ramchandra Rimal, Keshab R. Dahal

Arts & Sciences Faculty Publications

LSTM-SDM is a python-based integrated computational framework built on the top of Tensorflow/Keras and written in the Jupyter notebook. It provides several object-oriented functionalities for implementing single layer and multilayer LSTM models for sequential data modeling and time series forecasting. Multiple subroutines are blended to create a conducive user-friendly environment that facilitates data exploration and visualization, normalization and input preparation, hyperparameter tuning, performance evaluations, visualization of results, and statistical analysis. We utilized the LSTM-SDM framework in predicting the stock market index and observed impressive results. The framework can be generalized to solve several other real-world time series problems.

Realtime Event Detection In Sports Sensor Data With Machine Learning, Mallory Cashman

Honors Theses and Capstones

Machine learning models can be trained to classify time series based sports motion data, without reliance on assumptions about the capabilities of the users or sensors. This can be applied to predict the count of occurrences of an event in a time period. The experiment for this research uses lacrosse data, collected in partnership with SPAITR - a UNH undergraduate startup developing motion tracking devices for lacrosse. Decision Tree and Support Vector Machine (SVM) models are trained and perform with high success rates. These models improve upon previous work in human motion event detection and can be used a reference …

Estimating The Statistics Of Operational Loss Through The Analyzation Of A Time Series, Maurice L. Brown

Theses and Dissertations

In the world of finance, appropriately understanding risk is key to success or failure because it is a fundamental driver for institutional behavior. Here we focus on risk as it relates to the operations of financial institutions, namely operational risk. Quantifying operational risk begins with data in the form of a time series of realized losses, which can occur for a number of reasons, can vary over different time intervals, and can pose a challenge that is exacerbated by having to account for both frequency and severity of losses. We introduce a stochastic point process model for the frequency distribution …

A Brief Treatise On Bayesian Inverse Regression., Debashis Chatterjee Dr.

Doctoral Theses

Inverse problems, where in a broad sense the task is to learn from the noisy response about some unknown function, usually represented as the argument of some known functional form, has received wide attention in the general scientific disciplines. However, apart from the class of traditional inverse problems, there exists another class of inverse problems, which qualify as more authentic class of inverse problems, but unfortunately did not receive as much attention.In a nutshell, the other class of inverse problems can be described as the problem of predicting the covariates corresponding to given responses and the rest of the data. …

Some Nonparametric Hybrid Predictive Models : Asymptotic Properties And Applications., Tanujit Chakraborty Dr.

Doctoral Theses

Prediction problems like classification, regression, and time series forecasting have always attracted both the statisticians and computer scientists worldwide to take up the challenges of data science and implementation of complicated models using modern computing facilities. But most traditional statistical and machine learning models assume the available data to be well-behaved in terms of the presence of a full set of essential features, equal size of classes, and stationary data structures in all data instances, etc. Practical data sets from the domain of business analytics, process and quality control, software reliability, and macroeconomics, to name a few, suffer from various …

Analysis Of Eeg Data Using Complex Geometric Structurization, Eddy A. Kwessi, L. J. Edwards

Mathematics Faculty Research

Electroencephalogram (EEG) is a common tool used to understand brain activities. The data are typically obtained by placing electrodes at the surface of the scalp and recording the oscillations of currents passing through the electrodes. These oscillations can sometimes lead to various interpretations, depending on, for example, the subject’s health condition, the experiment carried out, the sensitivity of the tools used, or human manipulations. The data obtained over time can be considered a time series. There is evidence in the literature that epilepsy EEG data may be chaotic. Either way, the Embedding Theory in dynamical systems suggests that time series …

Functional Kernel Density Estimation: Point And Fourier Approaches To Time Series Anomaly Detection, Michael R. Lindstrom, Hyuntae Jung, Denis Larocque

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

We present an unsupervised method to detect anomalous time series among a collection of time series. To do so, we extend traditional Kernel Density Estimation for estimating probability distributions in Euclidean space to Hilbert spaces. The estimated probability densities we derive can be obtained formally through treating each series as a point in a Hilbert space, placing a kernel at those points, and summing the kernels (a “point approach”), or through using Kernel Density Estimation to approximate the distributions of Fourier mode coefficients to infer a probability density (a “Fourier approach”). We refer to these approaches as Functional Kernel Density …

Feasibility Of Multi-Year Forecast For The Colorado River Water Supply: Time Series Modeling, Brian Plucinski, Yan Sun, Shih-Yu (Simon) Wang, Robert R. Gilies, James Eklund, Chih-Chia Wang

Mathematics and Statistics Faculty Publications

The future of the Colorado River water supply (WS) affects millions of people and the US economy. A recent study suggested a cross-basin correlation between the Colorado River and its neighboring Great Salt Lake (GSL). Following that study, the feasibility of using the previously developed multi-year prediction of the GSL water level to forecast the Colorado River WS was tested. Time-series models were developed to predict the changes in WS out to 10 years. Regressive methods and the GSL water level data were used for the depiction of decadal variability of the Colorado River WS. Various time-series models suggest a …

Forecasting Crashes, Credit Card Default, And Imputation Analysis On Missing Values By The Use Of Neural Networks, Jazmin Quezada

Open Access Theses & Dissertations

A neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks,- also called Artificial Neural Networks - are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence, or AI. Recent studies shows that Artificial Neural Network has the highest coefficient of determination (i.e. measure to assess how well a model explains and predicts future outcomes.) in comparison to the K-nearest neighbor classifiers, logistic regression, discriminant analysis, naive Bayesian classifier, and classification trees. In this work, the theoretical description of the neural network methodology …

Modeling And Forecasting Crime Patterns In Bellingham, Washington, Zachary Domingo, Eric Shoner

Scholars Week

Our purpose is to use time series analysis to model and forecast the underlying dynamics behind crime in Bellingham, Washington. Using recent monthly data from the Bellingham Police Department, we considered singular spectrum analysis and autoregressive moving average modelling techniques to estimate significant deterministic patterns in the data. After examining the multitude of data provided, we narrowed down to two categories of crime: alcohol offenses and domestic violence. We created two time series models for each category and compared them to each other. The better performing model was used to forecast the number of crime incidents for ten months and …

Regime Switching In Cointegrated Time Series, Bradley David Zynda Ii

Undergraduate Honors Capstone Projects

Volatile commodities and markets can often be difficult to model and forecast given significant breaks in trends through time. To account such breaks, regime switching methods allow for models to accommodate abrupt changes in behavior of the data. However, the difficulty often arises in beginning the process of choosing a model and its associated parameters with which to represent the data and the objects of interest. To improve model selection for these volatile markets, this research examines time series with regime switching components and argues that a synthesis of vector error correction models with regime switching models with ameliorate financial …

An Inner-Outer Factorization In ℓp With Applications To Arma Processes, Raymond Cheng, William T. Ross

Department of Math & Statistics Faculty Publications

The following inner-outer type factorization is obtained for the sequence space ℓ^p: if the complex sequence F = (F₀, F₁,F₂,...) decays geometrically, then for an p sufficiently close to 2 there exists J and G in ℓ^p such that F = J * G; J is orthogonal in the Birkhoff-James sense to all of its forward shifts SJ, S²J, S³J, ...; J and F generate the same S-invariant subspace of ℓ^p; and G is a cyclic vector for S on ℓ …

Modelling The Links Between Inflation, Output Growth, Inflation Uncertainty And Output Growth Uncertainty In The Frameworks Of Regime-Switching And Multiple Structural Breaks: Evidence From The G7 Countries., Kushal Banik Chowdhury Dr.

Doctoral Theses

One of the long-standing and most investigated issues in macroeconomics is the nature of the relationship between inflation and output growth. Given this relationship as a central point of intense interest, one strand of studies has focused on the levels of the two series, while, more recently, an overgrowing body of research has highlighted the importance of the effects which are due to both the levels and the uncertainties associated with these two variables. These studies raise a number of interesting issues regarding the relationship between inflation and output growth. First, is there any direct effect of inflation on output …

Key Factors Driving Personnel Downsizing In Multinational Military Organizations, Ilksen Gorkem, Resit Unal, Pilar Pazos

Engineering Management & Systems Engineering Faculty Publications

Although downsizing has long been a topic of research in traditional organizations, there are very few studies of this phenomenon in military contexts. As a result, we have little understanding of the key factors that drive personnel downsizing in military setting. This study contributes to our understanding of key factors that drive personnel downsizing in military organizations and whether those factors may differ across NATO nations’ cultural clusters. The theoretical framework for this study was built from studies in non-military contexts and adapted to fit the military environment.

This research relies on historical data from one of the largest multinational …

Modelling Stock Returns In 'Volatility-In-Mean' Framework Under Up And Down Market Movements, Srikanta Kundu Dr.

Doctoral Theses

The first chapter of this thesis begins with a brief review of the existing literature on empirical studies on stock returns, especially those in the context of the relationship between risk and return, at both univariate and multivariate levels. In the next section, studies on the relationship between stock return and monetary policy are reviewed. The motivation of this work is discussed in Section 1.4. Finally, the format of the thesis is given in Section 1.5.A Brief Review of the Literature on Risk-Return Relationship-Both Univariate and Multivariate Cases. In this section, we first present a brief review of this literature …

Trends In Extreme U.S. Temperatures, Jaechoul Lee, Shanghong Li, Robert Lund

Mathematics Faculty Publications and Presentations

This paper develops trend estimation techniques for monthly maximum and minimum temperature time series observed in the 48 conterminous United States over the last century. While most scientists concur that this region has warmed on aggregate, there is no a priori reason to believe that temporal trends in extremes and averages will exhibit the same patterns. Indeed, under minor regularity conditions, the sample partial sum and maximum of stationary time series are asymptotically independent (statistically). Previous authors have suggested that minimum temperatures are warming faster than maximum temperatures in the United States; such an aspect can be investigated via the …

A Stochastic Parameter Regression Approach For Time-Varying Relationship Between Gold And Silver Prices, Birsen Canan-Mcglone

Boise State University Theses and Dissertations

In this thesis, we studied the gold and silver relationship using stochastic-parameter regression models. We formulated their time-varying relationship as a state-space model and used the Kalman filter algorithm to estimate the stochastic regression parameters for gold and silver prices. The data set used in this thesis covers 31 years using the London fix prices between January 1969 and December 2000. The start date was selected as the first full year silver prices were included in the London fix prices. Our stochastic parameter regression model explained well the time-varying relationship between gold and silver prices. As a special case of …

Detection Of Outliers In Time Series Data, Samson Sifael Kiware

Master's Theses (2009 -)

This thesis presents the detection of time series outliers. The data set used in this work is provided by the GasDay Project at Marquette University, which produces mathematical models to predict the consumption of natural gas for Local Distribution Companies (LDCs). Flow with no outliers is required to develop and train accurate models. GasDay is using statistical approaches motivated by normally distributed samples such as the 3 -sigma rule and the 5 -sigma rule to aid the experts in detecting outliers in residuals from the models. However, the Jarque-Bera statistical test shows that the residuals from the GasDay models are …

A Comparison Of Prediction Methods Of Functional Autoregressive Time Series, Devin Didericksen

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Functional data analysis (FDA) is a relatively new branch of statistics that has seen a lot of expansion recently. With the advent of computer processing power and more efficient software packages we have entered the beginning stages of applying FDA methodology and techniques to data. Part of this undertaking should include an empirical assessment of the effectiveness of some of the tools of FDA, which are sound on theoretical grounds. In a small way, this project helps advance this objective.

This work begins by introducing FDA, scalar prediction techniques, and the functional autoregressive model of order one - FAR(1). Two …

A Time Series Analysis Of The New Jersey Meadowlands Weather And Air Quality Data, Steven Spero

Theses, Dissertations and Culminating Projects

This research applies time series methods to determine relationships among a set of weather variables which are continually monitored in the Hackensack Meadowlands region of northern New Jersey. Weather data includes chemical and atmospheric factors. Chemical factors are Nitrogen Oxide, atmospheric Ozone, Carbon Monoxide, and Carbon Dioxide. Weather factors are wind speed, barometric pressure, air temperature, humidity, and solar radiation. Additionally, traffic density and time of week are brought in as categorical factors. This research attempts to (a) introduce the reader to various time series methodologies, (b) find a significant and efficient model for forecasting Nitrogen Oxide levels, and (c) …

Empirical Determination And Forecastability Of Foreign Exchange Rate Of India., Rituparna Kar Dr.

Doctoral Theses

The first chapter of this thesis begins with a brief review of the existing literature on foreign exchange rate models and their forecasting performance. Thereafter it presents the motivation as well as the main aspects of this study. The format of this chapter is as follows. A brief review of the relevant literature is presented in the first section. This review includes the important theoretical / structural as well as time series models of exchange rate. The motivation of the thesis is discussed in Section 1.2. Section 1.3 presents a brief account of the Indian economic reforms since 1993 with …

Predictability In The Indian Stock Market: A Study From An Econometric Perspective., Debabrata Mukhopadhyay Dr.

Doctoral Theses

No abstract provided.

Wavelet Techniques In Time Series Analysis With An Application To Space Physics, Agnieszka Jach

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Several wavelet techniques in the analysis of time series are developed and applied to real data sets.

Methods for long-memory models include wavelet-based confidence intervals for the self-similarity parameter in potentially heavy-tailed observations. Empirical coverage probabilities are used to assess the procedures by applying them to Linear Fractional Stable Motion with many choices of parameters. Asymptotic confidence intervals provide empirical coverage often much lower than nominal and it is recommended to use subsampling confidence intervals. A procedure for monitoring the constancy of the self-similarity parameter is proposed and applied to Ethernet data sets.

A test to distinguish a weakly dependent …

Is The World Evolving Discretely?, Saber Elaydi

Mathematics Faculty Research

No abstract provided.

Some Issues On Time Varying Risk Premium In Arch-M Model., Samarjit Das Dr.

Doctoral Theses

Since the 1970's it has been observed in many economies that financial and macroeconomic variables like equity prices, treasury bill rates and exchange rates have become more and more volatile in nature. This may be due tumor flexible monetary policies pursued in these countries as well as due to their increasing exposure towards various international developments. Accordingly, economic agents are facing increasingly more and more risky environment. Re- searchers as well as professional economists in the area of capital and business finance have, therefore, been increasingly attracted in recent years towards studying the effect of risk and uncertainty on asset …

Generalised Bootstrap Techniques., Singdhansu Bhusan Chatterjee Dr.

Doctoral Theses

A typical problem in statistics is as follows: there is some observable data Xn = (X1,..., Xn), and a parameter of interest θ which is related in such a way to the distribution of Xn that meaningful conclusions about θ can be drawn based on Xn. Sometimes data Xn is observed keeping the objective parameter θ in mind, at other times the parameter appears while trying to model the observed data.Once the data is observed and the parameter fixed, the questions that have to be addressed are as follows:(I) How to estimate θ from the data Xn?(II) Given an estimator …

Asymptotic Study Of Estimators In Some Discrete And Continuous Time Model., Arup Bose Dr.

Doctoral Theses

This thesis deals with an asymptotic study of estimatore in some discrete time and continuOus time models.The firet part deele with time eeriee date modelled by moving sverage and eutoregreseive proceseee. Higher order as ymptotioe beyond. as ymptotio normality, of the usual astimatore heve been dealt by Phillipe (1977,1978), Durbin (198o), Oohi (1983), Tanaka (1983), Fujikoshi and Ochi (1984). o(n-1/2) or o(n-1) expenaions ara 'obteined by these authore under the assumption of normality of orrors. The reason for this wes the lack of suitable. expaneions for normalized sums of dependent random vectora. Recently Gotza and Hipp (1983) heve been able …

Statistical Models For Consumer Behaviour In India., Amita Majumder Dr.

Doctoral Theses

The present dissertation is about analysis of consumer behaviour ba sed on time-serios consumption data in the framework of a static complete demand system with special reference to India.On the methodological side, the the proposes two station demand systems. One of them is based on a modification of the Simple Non-additivo Model (SNAM) of Deaton (1976) - a generalisation of Stone's Linear Expenditure System (LES) - that overcomes the limitations of the LES ariaing from the additivity of its underfying direct preference structure. The other system is based on the Frice Independent Generalized Linearity (FIGL) of Maellbaucr (1975). Both the …