Time-Series Analysis

Motor vehicle crashes are happening almost every day and unfortunately, some of them occur with fatality injuries. This paper presents a time series analysis on the trend and pattern of motor vehicle crash fatalities by month, day and day of the week in New York State. In this analysis, a statistical methodology for the decomposition of time series is used. The Kolmogorov-Zurbenko filter is used for decomposition of the crash fatalities into the long and seasonal and short-term component. Long-term component showed a 2 % decreases trend annually and seasonal term suggested increases of crash fatalities during the summer and decreases during winter period. The results indicate that sudden changes on trend are related to recessions that have occurred in USA. Another risk factor for crash fatalities in New York may be due to day light savings time that reduces light and visibility for drivers.

The agricultural fields in Egypt are commonly distributed with relatively small sizes parcels that usually reduce the reliability of Agricultural statistics in surveying cropland. The use of remote sensing help in an accurate crop inventory under complex landscape conditions based on the spectral characteristics differences of crops. The current study was carried out in Abu El Matamir district, Behira Governorate, located in western Nile delta Egypt. The main objective of the current study is using time series analysis of remote sensing data in crop discrimination. In this study, 160 locations of ground truth points collected during different growth stages of summer season crops. Two different sensors images used in this study represented by single date image of RapidEye and multi-date Landsat 8 OLI satellites. The acquired satellite images from both sensors atmospherically and geometrically corrected. Moreover different vegetation indices calculated such as Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index (EVI) for cultivated crops in the study area during the whole growing season. Preliminary statistical analysis applied to the collected field data to show the distribution of the cultivated crops types. Moreover, unsupervised Iso-Data applied for multi-date Landsat 8 OLI images and calculated VI's series for overall growth season. Results showed higher overall kappa accuracy with 0.82 and 0.79 respectively. NDVI showed the best representation of the crop pheno-logical changes during the crop growth season and showed higher accuracy in strategic crops discrimination than the single date image with higher spatial resolution.

Many still think of the Philippines as an agricultural economy, strictly speaking though, it is not anymore. Agriculture, fishery and forestry directly account for just one-fifth (20 percent) of the economy’s aggregate domestic output (GDP) today. With 70 percent of the country’s poor coming from the rural areas where agriculture is the dominant source of livelihood and employment, the challenge is to minimize agricultural import if not totally eliminate it specifically that of cereals not only to be able to elevate the conditions of the seventy percent of the poor but also for self-sufficiency.
The purpose of the study is to identify which of cereal yield, cereal productions, agriculture area and population significantly affect cereal import. Furthermore, this paper aims to find a model that could best explain the dependent variable using the said factors so as to know which areas should the government look into in order for the country to lessen its dependence on imports and thus have a more stable economy.
Several time-series statistical methods are used in modeling for the long-run and short-run dynamics of the cereal import: Augmented Dickey-Fuller test in testing for the unit root, Engle-Granger Approach for variables’ cointegration, Autoregressive (AR) process in modeling the lagged terms, and Error Correction Model (ECM) for the short-run effect model.

Keywords: Long-run and short-run dynamics, Augmented Dickey-Fuller Test, Engle-Granger Approach, AR Process, ECM

Las escalofriantes aventuras de Sabrina. Bienvenidos a un nuevo artículo. Como todos ya saben se acerca el 31 de octubre, la noche de Halloween, víspera del Día de Muertos, de Todos los Santos y fecha también conocida como la noche de las brujas. Y no sin razón, pues esta es una noche muy especial para ellas. La leyenda cuenta que las brujas se reúnen en aquelarres dos veces al año convocadas por el Diablo, el 30 de abril y el 31 de octubre. Fecha en la que se da la bienvenida a un nuevo año de brujería. En esta noche, los poderes satánicos y la brujería están en su nivel más alto. Así que, ahora ya lo saben; en este artículo se hablará de brujas, en concreto de Sabrina Spellan, una de las hechiceras más conocidas de la pantalla. Seguro que muchos de ustedes recuerdan a la encantadora de Sabrina de Sabrina, Cosas de Brujas (Sabrina, the Teenage Witch o Sabrina, la bruja adolescente) interpretada por Melissa Joan Hart. Esta serie de finales de los 90 era una divertida sitcom (serie cómica televisiva) que narraba las historias de una joven bruja, junto a sus tías Hilda y Zelda, su novio Harley Kinkle y su gato Salem. Pues Sabrina ha vuelto y de la mano de Netflix. Las Escalofriantes Aventuras de Aventuras de Sabrina o El Mundo Oculto de Sabrina como se conoce en Hispanoamérica (Chilling Aventures of Sabrina) Se estrenó el 26 de octubre de 2018 en Netflix, serie que es un tanto más oscura que la comedia. Aunque se repiten muchos de los personajes, nos espera un mundo lleno de magia negra, sombras y peligros ocultos con toques de terror, ingredientes perfectos para hacer una maratón de Halloween. La serie relata la oscura historia de la adolescente Sabrina, quien al cumplir 16 años se encuentra en un momento delicado su vida, tiene que elegir entre el mundo de la magia y el de los humanos, ya que ella es mitad bruja, mitad mortal. En esta adaptación, Sabrina luchará por conciliar sus dos universos mientras se enfrenta a las fuerzas malvadas que la amenazan a ella, a su familia y amigos y a toda la humanidad. Las Escalofriantes Aventuras de Sabrina, se desarrolla en el mismo universo en la que está inspirada la serie Riverdale. Los orígenes de la bruja adolescente también se encuentran en el cómic Archie de John L. Goldwater, una serie de historietas que empezaron a publicarse a principios de 1940 en Estados Unidos. Estas narran las aventuras de un grupo de adolescentes llamados Archie, Betty, Verónica y Judhead que viven en la ciudad de Riverdale. Sabrina Spellman es un personaje secundario de este cómic, que vive en un pueblo vecino llamado Greendale. Una joven de su época, valiente, persistente y honesta, dispuesta a luchar por sus amigos. Sabrina debutó en Archie's Madhouse #22 en octubre de 1962 y gustó tanto que finalmente se convirtió en uno de los personajes principales de Archie Comics. manuelverdugo.com

Sejak dioperasikan pada bulan Agustus 2 tahun lalu, Terminal 3 Ultimate yang dibangun megah, modern, dan bergaya futuristik itu tidaklah lepas dari masalah. Masalah kebocoran pada atap merupakan satu dari sekian banyak masalah. Masalah yang baru-baru ini terjadi adalah keretakan yang mengakibatkan bocor pada dinding beton underpass khusus kendaraan roda dua. Selain itu, sejumlah masalah yang pernah terjadi adalah listrik yang padam, plafon di area non-publik yang ambrol, ground tower unit milik PT Gapura Angkasa yang terbakar di apron, apron yang banjir hingga 20 cm karena saluran utama yang mampat. Adapun sejumlah keluhan dari masyarakat yang mengakses Terminal 3 Ultimate ini adalah lampu yang menempel di pilar-pilar besar Terminal 3 yang menyilaukan mata, pencahayaan di dalam terminal yang cenderung redup dan gelap, desain baggage claim yang 'melempar' barang bagasi ke bawah menghantam dinding berlapis karet, serta jarak dari area kedatangan ke gedung parkir yang cukup jauh (terutama bagi lansia). Untuk mengetahui bagaimana sebaiknya pengembangan fasilitas dilakukan, diperlukan peramalan mengenai jumlah penumpang keberangkatan internasional yang akan terjadi pada tahun 2025 di Bandara Internasional Soekarno-Hatta, yang merupakan materi dalam penelitian kecil ini.

In this paper, we will study dynamics of an important physiological control system—human gait in disease and aging. The investigation of fluctuations overlying periodic motion in human walking may provide valuable information about neuromuscular system generating both normal and pathological walking patterns. Using nonlinear dynamics analysis, we analyzed walking gaits of the young, elderly and aged subjects with Parkinson’s disease. This inquiry demonstrates that nonlinear time-series analysis methods based on time-delay embedding may provide useful insight into the neuromuscular control of human locomotion.

This paper examines the relationship among real GDP, CO 2 emissions, and energy use in the six Gulf Cooperation Council (GCC) countries. Using annual data for the years 1960–2013, stationarity, structural breaks, and cointegration tests have been conducted. The empirical evidence strongly supports the presence of unit roots. Cointegration tests reveal the existence of a clear long-run relationship only for Oman. Granger causality analysis shows that for three GCC countries (Kuwait, Oman, and Qatar) the predominance of the " growth hypothesis " emerges, since energy use drives the real GDP. Moreover, only for Saudi Arabia a clear long-run relation has not been discovered. Finally, the results of the variance decompositions and impulse response functions broadly confirm our previous empirical findings. Our results significantly reject the assumption that energy is neutral for growth. Notwithstanding, since the causality results are different for the six GCC countries, unified energy policies would not be the good recipe for the whole area.

Kebutuhan  masyarakat  akan  sarana  transportasi  pada  saat  ini  sangatlah penting. Salah satu alat transportasi paling umum digunakan di Indonesia asalah sepeda motor. Pada  saat  ini  kebutuhan  sepeda  motor  bagi  masyarakat terus  mengalami  peningkatan. Merk sepeda motor yang paling laris di Indonesia adalah Honda. Meski mengalami pasang surut dalam penjualannya, tetapi Honda selalu menguasai pangsa pasar di Indonesia hingga kini. Penelitian ini bertujuan untuk mendapatkan nilai peramalan penjualan sepeda motor Honda pada satu periode kedepan yaitu bulan juli 2016. Data yang digunakan adalah data penjualan sepeda motor Honda sejak bulan januari 2008 sampai dengan juni 2016. Metode yang digunakan adalah ARIMA. Metode ARIMA merupakan suatu metode yang menghasilkan ramalan-ramalan berdasarkan sintesis dari pola data secara historis. Hasil penelitian menunjukkan bahwa model yang didapatkan adalah ARIMA (0,1,1) yang dapat digunakan untuk memprediksi penjualan sepeda motor Honda bula juli 2016.

Three trials of each wheat, maize and potato were conducted from 2011 to 2014 in Agricultural research institute, Tarnab, Peshawar, Pakistan to evaluate the efficiency of alpha lattice design in field experiments. The results emphasize that the traditional randomized complete block designs (RCBD) should be replaced by alpha lattice in the agricultural field experiments when number of varieties to be tested in an experiment increases to more than ten. In such a situation finding a homogeneous block is quite difficult in field experiments. Alpha designs are used for field trials because they provide better control on experimental variability among the experimental units under field conditions.  The results show improvements in the precision level in terms of decline in the mean square error, coefficient of variation and standard error of difference. The coefficient of variation (CV) calculated for wheat, maize and potato yield trials are (9.20, 17.8 and 14.5) for alpha lattice design and (17.32, 23.70 and 18.53) for RCBD respectively. The standard error of mean squares calculated for these trials are (292, 3.67 and 2.41) for alpha lattice design and (437, 5.40 and 3.23) for randomized complete block design (RCBD) respectively. The relative efficiency of trials shows that alpha lattice design was more efficient than RCBD. The value of relative efficiency (1.49, 1.47 and 1.34) indicates that the use of alpha lattice design instead of randomized complete block design (RCBD) increased experimental efficiency by 49, 47 and 34 percent respectively.

The hyper-Russellian skeptic is someone who thinks that only one of all your experiences was, is, and will ever be conscious. Which one? The very one you are having now. Before you were always a zombie, and you will be a zombie for ever after. In the present literature on the metaphysics of passage of time, there is disagreement on whether our feeling that time passes — the " dynamic flavor " of our ordinary experience — provides support to the A-theory, that is, the thesis that the passage of time is an objective feature of reality. Lately, several philosophers have argued against this idea. In this paper I want to push this line of reasoning further by exploiting the hyper-Russellian scenario against the A-theory of time.

Drought is regarded as one of the most intangible and creeping natural disasters, which occurs in almost all climates, and its characteristics vary from region to region. The present study aims to investigate the effect of differentiation operations on improving the static and modeling accuracy of the drought index time series and after selecting the best selected model, evaluate drought severity and duration, as well as predict future drought behavior, in Semnan city. During this process, the effect of time series on modeling different monthly scales of drought index was analyzed, as well as the effect of differencing approach on stationarity improvement and prediction accuracy of the models. First, the stationarity of time series data related to a one-month drought index is investigated. By using seasonal, non-seasonal, and hybrid differencing, new time series are created to examine the improvement of the stationarity of these series through analyzing the ACF diagram and generalized Dickey–Fuller test. Based on the results, hybrid differencing indicates the best degree of stability. Then, the type and number of states required to evaluate the models are determined, and finally, the best prediction model is selected by applying assessment criteria. In the following, the same stages are analyzed for the drought index time series data derived from 6-month rainfall data. The results reveal that the SARIMA (2,0,2) (1,1,1)6 model with calibration assessment criteria of MAE = 0.510, RMSE = 0.752, and R = 0.218 is the best model for one-month data from seasonal differencing series. In addition to identifying and introducing the best time series model related to the six-month drought index data (SARIMA (3,0,5) (1,1,1)6 seasonal model with assessment criteria of MAE = 0.430, RMSE = 0.588, and R = 0.812), the results highlight the increased prediction accuracy of the six-month time seriesmodel by 4 times the correlation coefficient in the calibration section and 8 times that in the validation section, respectively, relative to the one-month state. Aftermodeling and comparing the results of the drought index between the selected model and the reality of the event, the severity and duration of the drought were also examined, and the results indicated a high agreement. Finally by applying the best six-month drought index model, a predicted series of the SPI drought index for the next 24 months is created.

Morris's Arc (1988) for String Quartet flows; it flows with substance, and with purpose. By flowing, I don’t just mean in the trivial sense that all music flows because it is unfolding in time and time flows. Rather I mean that there is continuity to its various surges of eventfulness; these surges are not disjunct or sudden. And by substance I mean both that its sound is as a sensuous fluid material and that the unfolding of its constituent events presents a coherent organization, thus binding together into a statement that seems meaningful in retrospect. By purpose I mean not only that there is a local logic to the consequence of one note following another, but also that the flow of events enables the material to organize into processes that forge a trajectory toward a goal.
    The analysis first focuses on local details considered in terms of pitch and pitch-class formations as arising from twelve-tone rows; then it delves into aspects of form, process, and texture. These are considered in terms of emergent properties in flux. The modeling of these properties and their flux employs some mathematical formalisms, but the reader can rely on the fact that every mathematical definition and formula is described carefully in prose as well.
    In Arc, the twelve-tone pitch class details and the surging emergent qualities projecting form are not completely separate from each other. Rather, all these features interact. Thus not only does the article weave together connections between such facets as form, process, and texture, but it also connects these to the twelve-tone rows and pitch-class configurations that are more straightforwardly part of the compositional plan (and more usual in analyses) of Morris’s music.

[Download the pdf below, or contact the author jmailman@alumni.uchicago.edu, for a digital offprint. It can also accessed through JSTOR: www.jstor.org/stable/10.7757/persnewmusi.52.2.0249 ]

We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in time series models and apply our modeling framework to daily realized measures of integrated variance. We develop asymptotic theory for parameter estimation and propose two model-building procedures. The methodology is applied to stocks of the Dow Jones Industrial Average during the period 2000 to 2009. We find strong evidence of nonlinear effects in financial volatility. An out-of-sample analysis shows that modeling these effects can improve forecast performance. Supplementary materials for this article are available online.

Penelitian ini meneliti tentang hubungan pengaruh antara emisi gas rumah kaca pembangkit listrik, konsumsi energi listrik, pertumbuhan ekonomi dan penduduk di Indonesia dengan menggunakan data time series tahun 1971-2011. Model analisis yang digunakan adalah model restricted VAR dan analisis structural VAR. Hasil uji menunjukkan bahwa konsumsi listrik, pertumbuhan ekonomi dan penduduk dapat mempengaruhi perubahan intensitas emisi gas rumah kaca. Dampak pengaruh dari penduduk terhadap emisi gas rumah kaca merupakan yang paling besar. Selain itu juga ditemukan bahwa pertumbuhan ekonomi dan penduduk dapat mempengaruhi besarnya konsumsi listrik, serta penduduk merupakan variabel yang dapat menentukan tingkat pertumbuhan ekonomi. Shock konsumsi listrik cenderung tidak berpengaruh terhadap perubahan intensitas emisi gas rumah kaca di beberapa waktu yang akan datang, sedangkan shock pertumbuhan ekonomi dapat dirasakan pengaruhnya oleh emisi gas rumah kaca dan penduduk dalam jangka pendek dan panjang respectively. Penduduk merupakan kontributor utama dalam mempengaruhi fluktuasi perubahan intensitas emisi gas rumah kaca di masa mendatang. Peneliti menyarankan pihak Pemerintah Indonesia untuk mewujudkan penggunaan energi listrik utama yang berasal dari sumber terbarukan dan mulai mengurangi penggunaan energi bahan bakar fosil. Lalu tugas besar pemerintah untuk mengurangi jumlah penduduk dan menekan pertumbuhan penduduk serta berupaya meningkatkan sumberdaya manusia yang berkualitas tinggi.

*Ini merupakan skripsi dalam format artikel/paper, edited pada Mei 2017. Untuk versi lengkapnya, silahkan mengunjungi link berikut ini dan ikuti prosedurnya http://etd.unsyiah.ac.id/index.php?p=show_detail&id=4500

Link to the code: https://github.com/mGalarnyk/Python_Tutorials/blob/master/Time_Series/Part2_Time_Series_Data_Price_Variation_ShiftingGroupBy.ipynb
Link to youtube video: https://www.youtube.com/watch?v=1S5UKLqe-gg

This code demonstrates how to view time series data in pandas as well as shifting dataframe, groupby datetime (daily, weekly, monthly), and price variation by day, month, year etc.

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

This paper applied an integrated method combining grey relation analysis, wavelet analysis and statistical analysis to study climate change and its effects on runoff at multi-time scales in the Kaidu River. Major findings are as follows: 1) Climatic factors were ranked in the order of importance to annual runoff as average annual temperature, average temperature in autumn, average temperature in winter, annual precipitation, flood season precipitation, average temperature in summer, and average temperature in spring. The average annual temperature and annual precipitation were selected as the two representative factors that impact the annual runoff. 2) From the 32-year time scale, the annual runoff and the average annual temperature presented a significantly rising trend, whereas the annual precipitation showed little increase over the period of 1957-2002. By changing the time scale from 32-year to 4-year, we observed nonlinear trends with increasingly obvious oscillations for annual runoff, average annual temperature, and annual precipitation. 3) The changes of the runoff and the regional climate are closely related, indicating that the runoff change is the result of the regional climate changes. With time scales ranging from 32-year, 16-year, 8-year and to 4-year, there are highly significant linear correlations between the annual runoff and the average annual temperature and the annual precipitation.

The aim of this paper is to assess the causal relationship among innovation in environment-related technologies, per capita income, and three major waste disposal operations (landfill, recycling, and incineration) for Korea. A time-series analysis over the frequency domain (Breitung-Candelon Spectral Granger causality) is applied, followed by Artificial Neural Networks experiments over the 1985-2016 period. Empirical results highlight that economic growth is tightly linked both to the growth of recycled waste and to the increase of environment-related innovations. Findings also highlight that waste recycling operations can spur the level of economic activity.

The negative effects on the macroeconomic indicators of the countries during the pandemic process that started with the Covid 19 epidemic made it necessary for individuals, investors, institutions, and governments to create a new policy. To determine the right policies, the effects of Covid 19 on the country's economy have been the subject of research by scientists. This study, it is aimed to investigate the effect of the pandemic on the stock market together with its sub-sectors. For this reason, in this study, the volatility of BIST100, known as the Turkish Stock Exchange, and its sub-indices, finance, service, and industry indices during the pandemic process were investigated. Working day data obtained from the Central Bank of the Republic of Turkey between 1/02/2020-10/11/2020 periods were used in the study. First, logarithmic transformation of the data was provided. Then, it was converted into a return series and included in the analysis.In order to obtain a consistent estimate, the series should not contain unit-roots. For this purpose, using the ADF unit root test, it was investigated whether the series contain unit root or not. According to the findings obtained from the study, it was determined that the most suitable variance model for BIST100 and Industrial indexes was the GARCH (1,1) model. It was found that the most suitable variance model in financial and services indices was the TARCH (1,1) model. In addition, while it is estimated that there is symmetrical volatility in BIST100 and Industrial indices, it is concluded that there are asymmetric and leverage effects in financial and services indices.

Actors in competitive environments are bound to decide and act under conditions of uncertainty because they rarely have accurate foreknowledge of how their opponents will respond and when they will respond. Just as a competitor makes a move to improve their standing on a given variable relative to a target competitor, she should expect the latter to counteract with an iterative lagged asymmetric response, that is, with a sequence of countermoves (iteration) that is very different in kind from its trigger (asymmetry) and that will be launched at some unknown point in the future (time lag). The paper explicates the broad relevance of the newly proposed concept of " iterative lagged asymmetric responses " to the social study of temporality and to fields as diverse as intelligence and counterintelligence studies, strategic management, futures studies, military theory, and long-range planning. By bringing out in the foreground and substantiating the observation that competitive environments place a strategic premium on surprise, the concept of iterative lagged asymmetric responses makes a contribution to the never-ending and many-pronged debate about the extent to which the future can be predicted.
DOI: https://doi.org/10.1177/0961463X17752652

Anthropogenic climate change is causing unprecedented rapid responses in marine
communities, with species across many different taxonomic groups showing faster
shifts in biogeographic ranges than in any other ecosystem. Spatial and temporal trends
for many marine species are difficult to quantify, however, due to the lack of long-term
datasets across complete geographical distributions and the occurrence of small-scale
variability from both natural and anthropogenic drivers. Understanding these changes
requires a multidisciplinary approach to bring together patterns identified within longterm
datasets and the processes driving those patterns using biologically relevant
mechanistic information to accurately attribute cause and effect. This must include likely
future biological responses, and detection of the underlying mechanisms in order to
scale up from the organismal level to determine how communities and ecosystems
are likely to respond across a range of future climate change scenarios. Using this multidisciplinary
approach will improve the use of robust science to inform the development
of fit-for-purpose policy to effectively manage marine environments in this
rapidly changing world.

This paper shows a methodology for short term load forecasting. The forecasting methodology proposed is based on the combined use of a fuzzy logic model for predicting the load profile up to 48 hours ahead, a polynomial neural network to predict daily values of minimum demand and peak demand and cubic splines interpolation for disaggregation of the hourly load forecasts in different time resolutions (10, 15 and 30 minutes). The performance of the proposed methodology is illustrated by the results from a computational experiment with the load records of the National Interconnected System (SIN).

This paper proposes an active contour algorithm for spectrogram track detection. It extends upon previously published work in a number of areas, previously published internal and potential energy models are refined and theoretical motivations for these changes are offered. These refinements offer a marked improvement in detection performance, including a notable reduction in the probability of false positive detections. The result is feature extraction at signal-to-noise ratios as low as− 1dB in the frequency domain.

In this paper we propose a flexible model to capture nonlinearities and long-range dependence in time series dynamics. The new model is a multiple regime smooth transition extension of the Heterogenous Autoregressive (HAR) model, which is specifically designed to model the behaviour of the volatility inherent in financial time series. The model is able to describe simultaneously long memory, as well as sign and size asymmetries. A sequence of tests is developed to determine the number of regimes, and an estimation and testing procedure is presented. Monte Carlo simulations evaluate the finite-sample properties of the proposed tests and estimation procedures. We apply the model to several Dow Jones Industrial Average index stocks using transaction level data from the Trades and Quotes database that covers ten years of data. We find strong support for long memory and both sign and size asymmetries. Furthermore, the new model, when combined with the linear HAR model, is viable and flexible for purposes of forecasting volatility.

Aging is one of the chief biomedical problems of the 21st century. After decades of basic research on biogerontology
(the science of aging), the aging process still remains an enigma. Although hundreds of "theories" on aging have
been formulated and many fundamental insights about age-related changes and genetic as well as environmental
interventions that change the pace of aging have been discovered, the actual why and how we age remain enigmatic.
In the post-genomic era there is an exponential increase in data. As a consequence it is a challenge to utilize all
information based on it and derive meaningful knowledge about biological phenomena. No individual scientist, no
group, nor consortium is capable of keeping up within their own field and are overwhelmed by the explosion of data
increase. Machine learning applied on biological data has the potential to solve this and cause a paradigm shift from
hypothesis-driven research (which predominates biological research including biogerontology) to data-driven
research.
This dissertation addresses this problem. In particular it proposes and executes the use of machine learning on
current existing data to predict drivers of aging (and therefore helps to distinguish causes from consequences),
interventions to counteract aging, and specific hypotheses to fill in research gaps that require experimental
validation.
The objective of this project is therefore to build computational models that are based on data relevant to the
phenomenon of aging and to predict as many of its aspects and dimensions as possible (thus elucidate their relations
to each other). For converting between and sorting within dimensions which are relevant to aging, different machine
learning models are evaluated. Ones models are built, it can be determined how much they can explain different
aspects of aging. Those models will also be capable of specifying which features are most relevant for prediction (in
both classification or regression). It is possible to train models that incorporate age-related changes based on
transcriptomic, proteomic, metabolomic, epigenomic as well as morphological data and their combinations. Machine
learning is further used to convert between and within them.
This work focuses on three types of predictors. Subsequently, discoveries are made with the statistical and learning
algorithms. The first model (lifespan predictor) is trained on predicting the lifespan based on genotype, environment
and combinations thereof. It is useful for predicting lifespan extending interventions on the population level. The
second model (age predictor) is trained on predicting the age given features measured on individuals. This is useful
for identifying biomarkers of aging and to determine the effects of interventions on the level of individuals. The third
model predicts functions/regulations of biological entities in regard to the aging process based on heterogeneous data
such as ontologies and diverse omics including time-series gene expression profiles (which can be visualized as
plots), and linked data. It is used to understand the role of genes and proteins as well as perhaps other entities such as
small molecules including lipids and other metabolites. Functions of proteins, which are still unknown, especially
those involved in yeast lipid metabolism and its regulation, can be predicted.
For this purpose we use primarily yeast as model organism as well as data on humans. Other biomedical model
organisms might be added if found beneficial.
The novel aspects of this research are for instance that 1) aging is investigated systematically in an unbiased
data-driven approach, 2) lifespan is predicted as continuous values, 3) age is predicted by combining multiple omics
data, 4) functions and regulations of biological entities like genes are predicted with high confidence from
heterogeneous data sources.
This thesis discovered that genetics is the most important feature of lifespan determination. Phenotypic features
related to lipid and membranes such as vacuolar morphology and autophagy activity are important for lifespan
determination according to the best performing models. A age predictor based on transcriptomics and proteomics can
highly accurately determine the age. It selected features are associated with both translation and lipid metabolism.
Among the top selected features are transcripts of genes when deleted exhibit abnormal vacuolar morphology as well
as targets of Opi1. Opi1 itself and its regulators were found to be differentially regulated post-transcriptional or
post-translational. Lastly, a function predictor for genes was created that achieved exceptional accuracy of
classifying aging genes. It learned for instance that piecemeal autophagy of the nucleus is strongly predictive for
aging-suppressor genes while cytoplasmic translation is strongly predictive for gerontogenes

In this work, the system design, integration, and wind tunnel experimental evaluation are presented for a bio-inspired self-sensing intelligent composite unmanned aerial vehicle (UAV) wing. A total of 148 micro-sensors, including piezoelectric, strain, and temperature sensors, in the form of stretchable sensor networks are embedded in the layup of a composite wing in order to enable its self-sensing capabilities. Novel stochastic system identification techniques based on time series models and statistical parameter estimation are employed in order to accurately interpret the sensing data and extract real-time information on the coupled airflow-structural dynamics. Special emphasis is given to the wind tunnel experimental assessment under various flight conditions defined by multiple airspeeds and angles of attack. A novel modeling approach based on the recently introduced Vector-dependent Functionally Pooled (VFP) model structure is employed for the stochastic identification of the " global " coupled airflow-structural dynamics of the wing and their correlation with dynamic flutter and stall. The obtained results demonstrate the successful system-level integration and e↵ectiveness of the stochastic identification approach, thus opening new perspectives for the state sensing and awareness capabilities of the next generation of " fly-by-feel " UAVs.

This paper analyzes how short-run interest rates are used to manage expectations of market participants in Turkey. The dynamic effects of short-run interest rates on the expected inflation, growth, exchange rate and interest rate for the period from August, 2001 to March, 2009 using Turkish data are investigated. It is claimed that the response of expected growth of the economy is negative to short-run interest rate increases, expected inflation is positive. When an interest rate shock is given to the expected exchange rate and interest rate, both exhibit persistent properties.