Combining Advanced Forecasting Methods with System Dynamics - the Case of Finnish Seaports

Seaports play an important part in the wellbeing of a nation. Many nations are highly dependent on foreign trade and most trade is done using sea vessels. This study is part of a larger research project, where a simulation model is required in order to create further analyses on Finnish macro logistical networks. The objective of this study is to create a system dynamic simulation model, which gives an accurate forecast for the development of demand of Finnish seaports up to 2030. The emphasis on this study is to show how it is possible to create a detailed harbor demand System Dynamic model with the help of statistical methods. The used forecasting methods were ARIMA (autoregressive integrated moving average) and regression models. The created simulation model gives a forecast with confidence intervals and allows studying different scenarios. The building process was found to be a useful one and the built model can be expanded to be more detailed. Required capacity for other parts of the Finnish logistical system could easily be included in the model.

Statistical Analysis of an SEIR Epidemic Model

This thesis was focussed on statistical analysis methods and proposes the use of Bayesian inference to extract information contained in experimental data by estimating Ebola model parameters. The model is a system of differential equations expressing the behavior and dynamics of Ebola. Two sets of data (onset and death data) were both used to estimate parameters, which has not been done by previous researchers in (Chowell, 2004). To be able to use both data, a new version of the model has been built. Model parameters have been estimated and then used to calculate the basic reproduction number and to study the disease-free equilibrium. Estimates of the parameters were useful to determine how well the model fits the data and how good estimates were, in terms of the information they provided about the possible relationship between variables. The solution showed that Ebola model fits the observed onset data at 98.95% and the observed death data at 93.6%. Since Bayesian inference can not be performed analytically, the Markov chain Monte Carlo approach has been used to generate samples from the posterior distribution over parameters. Samples have been used to check the accuracy of the model and other characteristics of the target posteriors.

Small and Large Scale Dynamics in the Milky Way

This thesis contains dynamical analysis on four different scales: the Solar system, the Sun itself, the Solar neighbourhood, and the central region of the Milky Way galaxy. All of these topics have been handled through methods of potential theory and statistics. The central topic of the thesis is the orbits of stars in the Milky Way. An introduction into the general structure of the Milky Way is presented, with an emphasis on the evolution of the observed value for the scale-length of the Milky Way disc and the observations of two separate bars in the Milky Way. The basics of potential theory are also presented, as well as a developed potential model for the Milky Way. An implementation of the backwards restricted integration method is shown, rounding off the basic principles used in the dynamical studies of this thesis. The thesis looks at the orbit of the Sun, and its impact on the Oort cloud comets (Paper IV), showing that there is a clear link between these two dynamical systems. The statistical atypicalness of the orbit of the Sun is questioned (Paper I), concluding that there is some statistical typicalness to the orbit of the Sun, although it is not very significant. This does depend slightly on whether one includes a bar, or not, as a bar has a clear effect on the dynamical features seen in the Solar neighbourhood (Paper III). This method can be used to find the possible properties of a bar. Finally, we look at the effect of a bar on a statistical system in the Milky Way, seeing that there are not only interesting effects depending on the mass and size of the bar, but also how bars can capture disc stars (Paper II).

Simulating the dynamics of silver market using computational market dynamics

Traditional econometric approaches in modeling the dynamics of equity and commodity markets, have, made great progress in the past decades. However, they assume rationality among the economic agents and and do not capture the dynamics that produce extreme events (black swans), due to deviation from the rationality assumption. The purpose of this study is to simulate the dynamics of silver markets by using the novel computational market dynamics approach. To this end, the daily data from the period of 1st March 2000 to 1st March 2013 of closing prices of spot silver prices has been simulated with the Jabłonska-Capasso-Morale(JCM) model. The Maximum Likelihood approach has been employed to calibrate the acquired data with JCM. Statistical analysis of the simulated series with respect to the actual one has been conducted to evaluate model performance. The model captures the animal spirits dynamics present in the data under evaluation well.