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.

Análisis de la fiabilidad de indicadores para sistemas de seguimiento de precios en los mercados

Para este trabajo se ha desarrollado un programa en Matlab, que nos permite realizar ensayos con algunas de las herramientas fundamentales del análisis técnico. Concretamente nos hemos centrado en el “Indicador de Movimiento Direccional” de Wilder. El programa está formado por seis funciones que permiten descargar datos, hacer la simulación del indicador, ajustar automáticamente algunos de sus parámetros y presentar los resultados obtenidos en la simulación. Con los experimentos y simulaciones realizadas se ha visto la importancia de escoger adecuadamente los períodos de ±DIs (indicadores direccionales positivo y negativo) y el ADX (Average Directional Movement Index). También hemos visto que la reglas decisión apuntadas por autores de reconocido prestigio como Cava y Ortiz ,no siempre se comportan como cabría esperar. Se propone mejorar el rendimiento y la fiabilidad de este indicador Incluyendo alguna media móvil de los precios y el volumen de contratación, en los criterios de decisión. También se podría mejorar implementando un sistema para que se pudiesen autoajustar los criterios de decisión.

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.

Is it possible to utilize VIX for a profitable trading strategy of S&P 500 Index?

The goal of this research was to make an overall sight to VIX® and how it can be used as a stock market indicator. Volatility index often referred as the fear index, measures how much it costs for investor to protect his/her S&P 500 position from fluctuations with options. Over the relatively short history of VIX it has been a successful timing coordinator and it has given incremental information about the market state adding its own psychological view of the amount of fear and greed. Correctly utilized VIX information gives a considerable advantage in timing market actions. In this paper we test how VIX works as a leading indicator of broad stock market index such as S&P 500 (SPX). The purpose of this paper is to find a working way to interpret VIX. The various tests are made on time series data ranging from the year 1990 to the year 2010. The 10-day simple moving average strategy gave significant profits from the whole time when VIX data is available. Strategy was able to utilize the increases of SPX in example portfolio value and was able to step aside when SPX was declining. At the times when portfolio was aside of S it was on safety fund like on treasury bills getting an annual yield of 3 percent. On the other side just a static number’s of VIX did not work as indicators in a profit making way.

Predicting Market Crashes: An Examination of the Performance of Macroeconomic Indicators and Technical Analysis

The purpose of this study is to examine macroeconomic indicators‟ and technical analysis‟ ability to signal market crashes. Indicators examined were Yield Spread, The Purchasing Managers Index and the Consumer Confidence Index. Technical Analysis indicators were moving average, Moving Average Convergence-Divergence and Relative Strength Index. We studied if commonly used macroeconomic indicators can be used as a warning system for a stock market crashes as well. The hypothesis is that the signals of recession can be used as signals of stock market crash and that way a basis for a hedging strategy. The data is collected from the U.S. markets from the years 1983-2010. Empirical studies show that macroeconomic indicators have been able to explain the future GDP development in the U.S. in research period and they were statistically significant. A hedging strategy that combined the signals of yield spread and Consumer Confidence Index gave most useful results as a basis of a hedging strategy in selected time period. It was able to outperform buy-and-hold strategy as well as all of the technical indicator based hedging strategies.

Orthogonal function method in modelling paper web quality variation

This work is devoted to the analysis of signal variation of the Cross-Direction and Machine-Direction measurements from paper web. The data that we possess comes from the real paper machine. Goal of the work is to reconstruct the basis weight structure of the paper and to predict its behaviour to the future. The resulting synthetic data is needed for simulation of paper web. The main idea that we used for describing the basis weight variation in the Cross-Direction is Empirical Orthogonal Functions (EOF) algorithm, which is closely related to Principal Component Analysis (PCA) method. Signal forecasting in time is based on Time-Series analysis. Two principal mathematical procedures that we used in the work are Autoregressive-Moving Average (ARMA) modelling and Ornstein–Uhlenbeck (OU) process.

Monitoring of mechanical sugarcane harvesting through control charts

ABSTRACT Statistical process control in mechanized farming is a new way to assess operation quality. In this sense, we aimed to compare three statistical process control tools applied to losses in sugarcane mechanical harvesting to determine the best control chart template for this quality indicator. Losses were daily monitored in farms located within Triângulo Mineiro region, in Minas Gerais state, Brazil. They were carried over a period of 70 days in the 2014 harvest. At the end of the evaluation period, 194 samples were collected in total for each type of loss. The control charts used were individual values chart, moving average and exponentially weighted moving average. The quality indicators assessed during sugarcane harvest were the following loss types: full grinding wheel, stumps, fixed piece, whole cane, chips, loose piece and total losses. The control chart of individual values is the best option for monitoring losses in sugarcane mechanical harvesting, as it is of easier result interpretation, in comparison to the others.

Liukuviin keskiarvoihin perustuvien kaupankäyntistrategioiden suoriutuminen Suomen osakemarkkinoilla

Tämän tutkimuksen tarkoituksena on selvittää, pystyykö teknistä analyysiä hyväksikäyttävä sijoittaja saamaan markkinatuottoa parempaa tuottoa Suomen osakemarkkinoilla. Tutkielman aineisto koostuu 24:stä vaihdetuimmasta osakkeesta Helsingin pörssissä. Nämä osakkeet muodostavat OMX25 –indeksin lukuun ottamatta yhtä osaketta, jota ei oltu vielä noteerattu tarkasteluperiodin alussa. Teknisen analyysin menetelminä käytetään neljää eripituista liukuvaa keskiarvoa (5, 20, 50 ja 100). Näistä muodostetaan liukuvien keskiarvojen kaksinkertaiset leikkausmenetelmät, joiden avulla saadaan osto- ja myyntisignaaleja kullekin osakkeelle. Tutkielman vertailukohteena käytetään perinteisen rahoitusteorian suosimaa osta ja pidä -strategiaa. Empiiristen testien tarkastelujakso on 1.1.2006 – 30.9.2010. Tutkielmassa havaittiin, että teknistä analyysiä hyväksikäyttäen voi saada markkinoita parempaa tuottoa, vaikka kaikki tulokset eivät olleet tilastollisesti merkittäviä. Tutkimuksessa ei otettu huomioon useista kaupoista syntyviä transaktiokustannuksia, veroja eikä korkotuottoa, jonka sijoittaja saisi pitäessään varoja esimerkiksi pankkitilillä ennen seuraavaa kauppaa. Erityisen huomionarvoista tässä tutkimuksessa oli se, että tekninen analyysi antoi sijoittajalle erittäin hyvän suojan finanssikriisin aikaiselta kurssilaskulta. Se antoi sijoittajalle selvän myyntisignaalin myydä osakkeet, ennen kuin kurssit alkoivat laskea rajusti.

Stochastic variability of output levels in a pulp mill

Quite often, in the construction of a pulp mill involves establishing the size of tanks which will accommodate the material from the various processes in which case estimating the right tank size a priori would be vital. Hence, simulation of the whole production process would be worthwhile. Therefore, there is need to develop mathematical models that would mimic the behavior of the output from the various production units of the pulp mill to work as simulators. Markov chain models, Autoregressive moving average (ARMA) model, Mean reversion models with ensemble interaction together with Markov regime switching models are proposed for that purpose.

Do retail investors use technical analysis? Evidence from Stuttgart stock exchange.

Tutkielma käyttää automaattista kuviontunnistusalgoritmia ja yleisiä kahden liukuvan keskiarvon leikkauspiste –sääntöjä selittääkseen Stuttgartin pörssissä toimivien yksityissijoittajien myynti-osto –epätasapainoa ja siten vastatakseen kysymykseen ”käyttävätkö yksityissijoittajat teknisen analyysin menetelmiä kaupankäyntipäätöstensä perustana?” Perusolettama sijoittajien käyttäytymisestä ja teknisen analyysin tuottavuudesta tehtyjen tutkimusten perusteella oli, että yksityissijoittajat käyttäisivät teknisen analyysin metodeja. Empiirinen tutkimus, jonka aineistona on DAX30 yhtiöiden data vuosilta 2009 – 2013, ei tuottanut riittävän selkeää vastausta tutkimuskysymykseen. Heikko todistusaineisto näyttää kuitenkin osoittavan, että yksityissijoittajat muuttavat kaupankäyntikäyttäytymistänsä eräiden kuvioiden ja leikkauspistesääntöjen ohjastamaan suuntaan.

Effect of air pollution on pediatric respiratory emergency room visits and hospital admissions

In order to assess the effect of air pollution on pediatric respiratory morbidity, we carried out a time series study using daily levels of PM10, SO2, NO2, ozone, and CO and daily numbers of pediatric respiratory emergency room visits and hospital admissions at the Children's Institute of the University of São Paulo Medical School, from August 1996 to August 1997. In this period there were 43,635 hospital emergency room visits, 4534 of which were due to lower respiratory tract disease. The total number of hospital admissions was 6785, 1021 of which were due to lower respiratory tract infectious and/or obstructive diseases. The three health end-points under investigation were the daily number of emergency room visits due to lower respiratory tract diseases, hospital admissions due to pneumonia, and hospital admissions due to asthma or bronchiolitis. Generalized additive Poisson regression models were fitted, controlling for smooth functions of time, temperature and humidity, and an indicator of weekdays. NO2 was positively associated with all outcomes. Interquartile range increases (65.04 µg/m³) in NO2 moving averages were associated with an 18.4% increase (95% confidence interval, 95% CI = 12.5-24.3) in emergency room visits due to lower respiratory tract diseases (4-day moving average), a 17.6% increase (95% CI = 3.3-32.7) in hospital admissions due to pneumonia or bronchopneumonia (3-day moving average), and a 31.4% increase (95% CI = 7.2-55.7) in hospital admissions due to asthma or bronchiolitis (2-day moving average). The study showed that air pollution considerably affects children's respiratory morbidity, deserving attention from the health authorities.

Identification of anaerobic threshold using heart rate response during dynamic exercise

The objective of the present study was to characterize the heart rate (HR) patterns of healthy males using the autoregressive integrated moving average (ARIMA) model over a power range assumed to correspond to the anaerobic threshold (AT) during discontinuous dynamic exercise tests (DDET). Nine young (22.3 ± 1.57 years) and 9 middle-aged (MA) volunteers (43.2 ± 3.53 years) performed three DDET on a cycle ergometer. Protocol I: DDET in steps with progressive power increases of 10 W; protocol II: DDET using the same power values as protocol 1, but applied randomly; protocol III: continuous dynamic exercise protocol with ventilatory and metabolic measurements (10 W/min ramp power), for the measurement of ventilatory AT. HR was recorded and stored beat-to-beat during DDET, and analyzed using the ARIMA (protocols I and II). The DDET experiments showed that the median physical exercise workloads at which AT occurred were similar for protocols I and II, i.e., AT occurred between 75 W (116 bpm) and 85 W (116 bpm) for the young group and between 60 W (96 bpm) and 75 W (107 bpm) for group MA in protocols I and II, respectively; in two MA volunteers the ventilatory AT occurred at 90 W (108 bpm) and 95 W (111 bpm). This corresponded to the same power values of the positive trend in HR responses. The change in HR response using ARIMA models at submaximal dynamic exercise powers proved to be a promising approach for detecting AT in normal volunteers.

Parameter identification in time series models

Time series analysis can be categorized into three different approaches: classical, Box-Jenkins, and State space. Classical approach makes a basement for the analysis and Box-Jenkins approach is an improvement of the classical approach and deals with stationary time series. State space approach allows time variant factors and covers up a broader area of time series analysis. This thesis focuses on parameter identifiablity of different parameter estimation methods such as LSQ, Yule-Walker, MLE which are used in the above time series analysis approaches. Also the Kalman filter method and smoothing techniques are integrated with the state space approach and MLE method to estimate parameters allowing them to change over time. Parameter estimation is carried out by repeating estimation and integrating with MCMC and inspect how well different estimation methods can identify the optimal model parameters. Identification is performed in probabilistic and general senses and compare the results in order to study and represent identifiability more informative way.

Effect of air pollution on diabetes and cardiovascular diseases in São Paulo, Brazil

Type 2 diabetes increases the risk of cardiovascular mortality and these patients, even without previous myocardial infarction, run the risk of fatal coronary heart disease similar to non-diabetic patients surviving myocardial infarction. There is evidence showing that particulate matter air pollution is associated with increases in cardiopulmonary morbidity and mortality. The present study was carried out to evaluate the effect of diabetes mellitus on the association of air pollution with cardiovascular emergency room visits in a tertiary referral hospital in the city of São Paulo. Using a time-series approach, and adopting generalized linear Poisson regression models, we assessed the effect of daily variations in PM10, CO, NO2, SO2, and O3 on the daily number of emergency room visits for cardiovascular diseases in diabetic and non-diabetic patients from 2001 to 2003. A semi-parametric smoother (natural spline) was adopted to control long-term trends, linear term seasonal usage and weather variables. In this period, 45,000 cardiovascular emergency room visits were registered. The observed increase in interquartile range within the 2-day moving average of 8.0 µg/m³ SO2 was associated with 7.0% (95%CI: 4.0-11.0) and 20.0% (95%CI: 5.0-44.0) increases in cardiovascular disease emergency room visits by non-diabetic and diabetic groups, respectively. These data indicate that air pollution causes an increase of cardiovascular emergency room visits, and that diabetic patients are extremely susceptible to the adverse effects of air pollution on their health conditions.