Historical logging, productivity, and structural characteristics of boreal coniferous forests in Norway

Abstract

Geometrically accurate time series of archived aerial images and airborne lidar data in a forest environment

Abstract

Kysynnän ennustaminen leipomotuotteille ja ennusteen tarkkuuden arviointi

Tässä diplomityössä tutkittiin kysynnän ennustamista Vaasan & Vaasan Oy:n tuotteille. Ensin työssä perehdyttiin ennustamiseen ja sen tarjoamiin mahdollisuuksiin yrityksessä. Erityisesti kysynnän ennustamisesta saatavat hyödyt käytiin läpi. Kysynnän ennustamisesta haettiin ratkaisua erityisesti ongelmiin työvuorosuunnittelussa.Työssä perehdyttiin ennustemenetelmiin liittyvään kirjallisuuteen, jonka oppien perusteella tehtiin koe-ennustuksia yrityksen kysynnän historiadatan avulla. Koe-ennustuksia tehtiin kuudelle eri Turun leipomon koe-tuotteelle. Ennustettavana aikavälinä oli kahden viikon päiväkohtainen kysyntä. Tämän aikavälin erityisesti peruskysynnälle etsittiin ennustetarkkuudeltaan parasta kvantitatiivista ennustemenetelmää. Koe-ennustuksia tehtiin liukuvilla keskiarvoilla, klassisella aikasarja-analyysillä, eksponentiaalisen tasoituksen menetelmällä, Holtin lineaarisella eksponenttitasoituksen menetelmällä, Wintersin kausittaisella eksponentiaalisella tasoituksella, autoregressiivisillä malleilla, Box-Jenkinsin menetelmällä ja regressioanalyysillä. Myös neuroverkon opettamista historiadatalla ja käyttämistä ongelman ratkaisun apuna kokeiltiin.Koe-ennustuksien tulosten perusteella ennustemenetelmien toimintaa analysoitiin jatkokehitystä varten. Ennustetarkkuuden lisäksi arvioitiin mallin yksinkertaisuutta, helppokäyttöisyyttä ja sopivuutta yrityksen monien tuotteiden ennustamiseen. Myös kausivaihteluihin, trendeihin ja erikoispäiviin kiinnitettiin huomiota. Ennustetarkkuuden huomattiin parantuvan selvästi peruskysyntää ennustettaessa, jos ensin historiadata esikäsittelemällä puhdistettiin erikoispäivistä ja –viikoista.

Integrating Socket Server to the Workstation Environment of Series 40 Platform

Shortening development times of mobile phones are also accelerating the development times of mobile phone software. New features and software components should be partially implemented and tested before the actual hardware is ready. This brings challenges to software development and testing environments, especially on the user interface side. New features should be able to be tested in an environment, which has a look and feel like a real phone. Simulation environments are used to model real mobile phones. This makes possible to execute software in a mobile phone that does not yet exist. The purpose of this thesis is to integrate Socket Server software component to Series 40 simulation environments on Linux and Windows platforms. Socket Server provides TCP/IP connectivity for applications. All other software and hardware components below Socket Server do not exist in simulation environments. The scope of this work is to clarify how that can be done without connectivity problems, including design, implementation and testing phases.

Value at Risk in measuring market risk: Historical simulation

In this study the theoretical part was created to make comparison between different Value at Risk models. Based on that comparison one model was chosen to the empirical part which concentrated to find out whether the model is accurate to measure market risk. The purpose of this study was to test if Volatility-weighted Historical Simulation is accurate in measuring market risk and what improvements does it bring to market risk measurement compared to traditional Historical Simulation. Volatility-weighted method by Hull and White (1998) was chosen In order to improve the traditional methods capability to measure market risk. In this study we found out that result based on Historical Simulation are dependent on chosen time period, confidence level and how samples are weighted. The findings of this study are that we cannot say that the chosen method is fully reliable in measuring market risk because back testing results are changing during the time period of this study.

Time-Varying Benefits of International Diversification for Finnish Investor: Country-Specific Strategy Concentrating on Emerging Markets

The main objective of this study is to investigate whether the Finnish investors’ country-specific strategy concentrating on emerging markets provides diversification benefits. We also analyze whether the benefits of international diversification has been diminished after periods of high volatility caused by different market crisis. The objective is investigated with three methods: Correlation coefficients, rolling correlations added with OLS trend-lines and Box’s M statistic. All the empirical tests are analyzed and calculated with logarithmic returns of weekly time series data from Friday closing values between January 1995 and December 2007. The number of weekly observations is 678. The data type is total return indices of different countries. Data is collected from DataStream and provided by Datastream Financial. Countries investigated are Finland, Argentina, Brazil, Chile, China, India, Mexico, Poland, Russia, South Africa, South Korea, Thailand and Turkey. The current data is quoted both in U.S. Dollars and local currencies. The empirical results of this thesis show that the correlation coefficients are time-varying across Finland and 12 emerging market countries. Although the correlations have risen from 1995 to 2007, there can be found sub-periods where the correlation has declined from earlier period. The results also indicate that a Finnish investor constructing a portfolio of emerging market countries cannot rely on the correlation coefficients estimated from historical data because of the instability of correlation matrices.

Analysis of patterns in mining time series using Qlucore

Raw measurement data does not always immediately convey useful information, but applying mathematical statistical analysis tools into measurement data can improve the situation. Data analysis can offer benefits like acquiring meaningful insight from the dataset, basing critical decisions on the findings, and ruling out human bias through proper statistical treatment. In this thesis we analyze data from an industrial mineral processing plant with the aim of studying the possibility of forecasting the quality of the final product, given by one variable, with a model based on the other variables. For the study mathematical tools like Qlucore Omics Explorer (QOE) and Sparse Bayesian regression (SB) are used. Later on, linear regression is used to build a model based on a subset of variables that seem to have most significant weights in the SB model. The results obtained from QOE show that the variable representing the desired final product does not correlate with other variables. For SB and linear regression, the results show that both SB and linear regression models built on 1-day averaged data seriously underestimate the variance of true data, whereas the two models built on 1-month averaged data are reliable and able to explain a larger proportion of variability in the available data, making them suitable for prediction purposes. However, it is concluded that no single model can fit well the whole available dataset and therefore, it is proposed for future work to make piecewise non linear regression models if the same available dataset is used, or the plant to provide another dataset that should be collected in a more systematic fashion than the present data for further analysis.

MCMC Analysis Of Classical Time Series Algorithms

Identification of order of an Autoregressive Moving Average Model (ARMA) by the usual graphical method is subjective. Hence, there is a need of developing a technique to identify the order without employing the graphical investigation of series autocorrelations. To avoid subjectivity, this thesis focuses on determining the order of the Autoregressive Moving Average Model using Reversible Jump Markov Chain Monte Carlo (RJMCMC). The RJMCMC selects the model from a set of the models suggested by better fitting, standard deviation errors and the frequency of accepted data. Together with deep analysis of the classical Box-Jenkins modeling methodology the integration with MCMC algorithms has been focused through parameter estimation and model fitting of ARMA models. This helps to verify how well the MCMC algorithms can treat the ARMA models, by comparing the results with graphical method. It has been seen that the MCMC produced better results than the classical time series approach.

Regime Switching Models for Electricity Time Series that Capture Fat Tailed Distributions

In the power market, electricity prices play an important role at the economic level. The behavior of a price trend usually known as a structural break may change over time in terms of its mean value, its volatility, or it may change for a period of time before reverting back to its original behavior or switching to another style of behavior, and the latter is typically termed a regime shift or regime switch. Our task in this thesis is to develop an electricity price time series model that captures fat tailed distributions which can explain this behavior and analyze it for better understanding. For NordPool data used, the obtained Markov Regime-Switching model operates on two regimes: regular and non-regular. Three criteria have been considered price difference criterion, capacity/flow difference criterion and spikes in Finland criterion. The suitability of GARCH modeling to simulate multi-regime modeling is also studied.

Analysis of Patterns in Electricity Spot Market Time Series

Due to its non-storability, electricity must be produced at the same time that it is consumed, as a result prices are determined on an hourly basis and thus analysis becomes more challenging. Moreover, the seasonal fluctuations in demand and supply lead to a seasonal behavior of electricity spot prices. The purpose of this thesis is to seek and remove all causal effects from electricity spot prices and remain with pure prices for modeling purposes. To achieve this we use Qlucore Omics Explorer (QOE) for the visualization and the exploration of the data set and Time Series Decomposition method to estimate and extract the deterministic components from the series. To obtain the target series we use regression based on the background variables (water reservoir and temperature). The result obtained is three price series (for Sweden, Norway and System prices) with no apparent pattern.

Dental Health in Primary Teeth after Prevention of Mother-Child Transmission of Mutans Streptococci. A Historical Cohort Study on Restorative Visits and Maternal Prevention Costs

Mutansstreptokokkitartunnan ehkäisemisen pitkäaikaisvaikutukset maitohampaiden terveyteen. Kohorttitutkimus korjaavan hoidon määrästä ja kariesehkäisyn kustannuksista. Tutkimuksen tarkoituksena oli selvittää varhaisen mutansstreptokokki (MS)-kolonisaation ehkäisyn pitkäaikaisvaikutuksia korkean kariesriskin omaavien lasten maitohampaistossa sekä tarkastella MS-tartunnan estämisen kustannuksia. Tiedot lasten hampaiden terveydestä ja hammashoitotoimenpiteistä syntymästä 10-vuotiaaksi sekä äiteihin kohdistuneen kariesehkäisyn kustannuksista kerättiin Ylivieskan terveyskeskuksen asiakirjoista. Tutkimuksessa oli mukana yhteensä 507 lasta, heistä 148 oli osallistunut aikaisempaan Ylivieskan äiti-lapsitutkimukseen, jossa verrattiin äitien käyttämän ksylitolipurukumin ja äidille tehtyjen fluori- tai klooriheksidiinilakkausten vaikutusta pikkulasten hampaiden terveyteen. Maitohammaskariesta esiintyi 10-vuotiaaksi asti merkitsevästi vähemmän lapsilla, jotka eivät olleet saaneet MS-tartuntaa alle 2-vuotiaana, heidän maitohampaansa säilyivät 3,4 vuotta kauemmin täysin ehjinä (p<0.001) ja he tarvitsivat vähemmän maitohampaiden korjaavaa hoitoa (p=0.005) kuin lapset, joiden hampaisto oli kolonisoitunut MS-bakteerilla jo 2-vuotiaana. Koska ksylitoliryhmän lasten MS-kolonisaatio oli vähäisintä, heidän maitohampaissaan oli vähemmän kariesta ja korjaavan hoidon tarvetta kuin kahden muun korkeariskisen ryhmän lapsilla. Äitien käyttämän ksylitolipurukumin kustannukset olivat yhteensä 116 euroa ja lapsen maitohampaiden säilyminen täysin ehjinä vuoden pidempään maksoi 37 euroa. Kun MS-tartunta oli saatu estettyä, korkean kariesriskin omaavien lasten hampaiden terveys oli samalla tasolla kuin keskimäärin koko ikäkohortilla. Lapsen maitohampaat säilyvät terveinä pidempään ja korjaavan hoidon tarve vähenee, kun MS-kolonisaatio alle 2-vuotiaana saadaan estettyä. Lapsen MS-kolonisaatio vähenee merkitsevästi, kun äiti käyttää ksylitolipurukumia lapsen ollessa 0-2 vuoden ikäinen, siten pikkulapsen äidin säännöllinen ksylitolipurukumin käyttö saattaa olla julkisen tereydenhuollon kannalta tarkoituksenmukainenterveyttä edistävä menetelmä.

The Fed Model: International Analysis

The Fed model is a widely used market valuation model. It is often used only on market analysis of the S&P 500 index as a shorthand measure for the attractiveness of equity, and as a timing device for allocating funds between equity and bonds. The Fed model assumes a fixed relationship between bond yield and earnings yield. This relationship is often assumed to be true in market valuation. In this paper we test the Fed model from historical perspective on the European markets. The markets of the United States are also includedfor comparison. The purpose of the tests is to determine if the Fed model and the underlying assumptions come true on different markets. The various tests are made on time-series data ranging from the year 1973 to the end of the year 2008. The statistical methods used are regressions analysis, cointegration analysis and Granger causality. The empirical results do not give strong support for the Fed model. The underlying relationships assumed by the Fed model are statistically not valid in most of the markets examined and therefore the model is not valid in valuation purposes generally. The results vary between the different markets which gives reason to suspect the general use of the Fed model in different market conditions and in different markets.