With the development of the financial markets, the foreign exchange market has become more and more important for investors. The daily volume of business dealt with on the foreign exchange markets in 1998 was estimated to be over $2.5 trillion dollars (the daily volume on New York Stock Exchanges is about $20 billion). Today (2006) it may be about $5 trillion dollars. More and more people notice the foreign exchange market, and more and more sophisticated investors research such markets. The purpose of this thesis is to compare different methods to forecast the exchange rate of the money pair AUD/JPY. Firstly we studied the relationship between the AUD/JPY exchange rate and some economic fundamentals by using a regression model. Secondly, we tested whether the AUD/JPY exchange rate had any relationship with its historical records by using an ARIMA model. Finally, we compared the two model forecasting performance. A secondary purpose is to test whether the Market Efficiency Hypothesis works on the money pair AUD/JPY. In the study, data from January 1986 to June 2006 were chosen. To test which method produces better forecasts, we chose data from January 1986 to December 2002 to build up the prediction functions. Then we used the data from January 2003 to 2006 June to evaluate which predicting method was closer to the reality. In the comparison of the forecasting performances, two approaches dealing with the unknown future fundamentals were used. Firstly we assumed that we could do perfect predictions of these regressors, that was, our predictions of these regressors were the same as the actual future outcomes. So we put the real data for the fundamentals from January 2003 to June 2006 into the regression function. Secondly we assumed that we were in real life situation, and we had to predict the regressors first in order to get the predictions of the exchange rate. The results of the comparison were that the AUD/JPY exchange rate could to some extent be predictable, and that the predictions by the ARIMA model were more accurate.

I denna uppsats har olika metoder för att göra prognoser för ekonomiska tidsserier med säsongsmönster jämförts och utvärderats. Frågan som undersökningen har kretsat kring är: Vilken metod är bäst lämpad för att göra prognoser av tidsserier med säsongsmönster? De metoder som jämförs är säsongsrensningsmetoderna Census II och TRAMO/SEATS, säsongsmodellerna SARIMA och ARIMA med dummyvariabler för säsong samt en metod där medelvärdena från de fyra första metoderna används som prognoser. För att genomföra undersökningen har dessa metoder tillämpats på fyra ekonomiska tidsserier, nämligen: konsumtion, BNP, export samt byggstarter. Resultatet från undersökningen är att säsongsmodellerna är bäst för konsumtionsserien, säsongsrensningsmetoderna är bäst för BNP- och exportserien och den ena säsongsmodellen (SARIMA) är bäst för byggstartsserien medan den andra (ARIMA-dummy) är den sämsta. Val av prognosmetod beror med andra ord på vilken serie som ska prognostiseras.

In this paper, we focus on the goodness of fit test for self-similar property of two well-known processes: the fractional Brownian motion and the fractional autoregressive integrated moving average process. The Hurst parameter of the self-similar process is estimated by the embedding branching process method proposed by Jones and Shen (2004). The goodness of fit test for self-similarity is based on the Pearson chi-square test statistic. We approximate the null distribution of the test statistic by a scaled chi-square distribution to correct the size bias problem of the conventional chi-square distribution. The scale parameter and degrees of freedom of the test statistic are determined via regression method. Simulations are performed to show the finite sample size and power of the proposed test. Empirical applications are conducted for the high frequency financial data and human heart rate data.

This study investigates the trends and varieties of concentration of ozone in recent years in Taiwan in order to understand the situation of air qualities in different areas. Ozone is the secondary pollutant produced by nitrogen oxides, reactive hydrocarbons and sunlight. Because ozone has strong oxidizing power, it is easy to stimulate the respiratory system, which may cause cough, asthma, headache, tiredness and harmful to lung; and it is also harmful to plants and even synthetic materials. Here, we tried to study the trends and varieties of the time effect to the ozone level in each region and compare the similarities and heterogeneity of the models in different regions by the ozone data obtained from all air quality monitoring stations of environmental protection administration. Analysis of building appropriate temporal and spatial models are performed and factor analysis on the model residuals are used to investigate the possible latent variables to interpret the patterns of the ozone values in different regions. These may help to set up strategies for ozone control in the future.

Abstract The impenetrable movement and crash of the stock market is always the most intriguing research task of any financial researcher. Nowadays, it has been proved that the movements of financial asset have the property of non-linearity or near-chaos and shows some tendency within a given period. We used the R/S analysis as the tool to indicate the tendency, and those stocks as our researching objects. We then combined purely price technical analysis indicators and genetic algorithms to form a predicting model. Then we compared our genetic predicting model with the traditional ARIMA analysis and hope to find out the invisible pattern under price volatility. And we hope our model could assist investors in assessing the stock markets more objectively and reduce the risk of stock investment. The researching target is TSMC(2330). We covered the period from 5 September 1994 to 28 December 1999, resulting in 1490 trading days. Historical data are available from Taiwan Economic Journal (TEJ). We execute the researching comparison by bear-market, bull-market, and bull-then-bear market and concluded as follows. 1. After the R/S analysis, we got the Hurst exponent of TSMC to be 0.849855 and the trending cycle was 940. It has proved that the market has tendency and indirectly showed that the Taiwan stock market was not efficient. 2. According to directional precision, our predicting model apparently outpaced the ARIMA model in these three periods. The reason was that our model grabbed more information than the ARIMA model. 3. If we only think about the inputs and outputs, our model seems to be a proper framework for explaining the relationships among variables in comparison with the neural network model having the same input and output variables. 4. We can deduce the invisible relationships of price technical indicators and the closing price. 5. Genetic predicting model can detect the prevailing trend of the learning periods. 6. The shorter the learning period, the better the predicting effects. As a whole and conservatively speaking, we have 70% confidence in directional precision. 7. If we combine proper trading strategy with genetic predicting model and deduct the transaction cost, we still get a better profit than buy-and-hold strategy and have some maneuvering flexibility. 8. After hypothesis testing, our predicting model seems to have some potential of ex ante prediction, but the stability and usability still need further study. In short, we proposed the ex post stock price movement learning model and the viable direction of ex ante prediction. Investors can take advantage of the flexibility of the predicting model and avoid using the over-complex and rigid trading strategies.

Att i förväg försöka förutsäga vilket lag som kommer vinna i en fotbollsmatch har nog de flesta försökt sig på någon gång. Att gissa och att faktiskt försöka att analysera båda lagens förutsättningar är två väldigt olika metoder att komma fram till sitt resultat. I och med att datorkraften de senaste åren kraftigt förbättrats har det också kommit fler och framför allt tyngre matematiska modeller för att skatta utfallet av matcher. I detta examensarbete används Pi-ratingsystemet som går ut på att varje lag får en rating för hur bra man är på hemma- respektive bortaplan. Som en utveckling av den ursprungliga Pi-rating modellen används det i detta arbete tre olika modeller för att prediktera lagens framtida rating. Modellerna som används är enkelt glidande medelvärde, enkel exponentiell utjämning och en ARIMA-modell. En lösning på hur nya lag som inte spelade i ligan föregående år ska behandlas föreslås också. Avslutningsvis diskuteras olika investeringsmetoder som kan användas för att använda resultat från modellerna på marknaden för vadslagning. Resultatet visar att en spelstrategi som utnyttjat Kellys formel ger störst avkastning för kalibreringsdatat. När denna strategi används på matcher utanför kalibreringsåren visar resultatet på en mycket låg vinst och framför allt att vinsten under lång tid är negativ, vilket från en investeringssyn inte är något man önskar. Sammanfattningsvis är denna metod inte i sig själv tillräckligt bra för att ge en säker avkastning men är en bra grund som kan byggas ut för att ta hänsyn till fler faktorer och då ge möjlighet till stabilare och mer långsiktiga vinster. / To predict a soccer game in advance is something that has been done by most people. If the prediction is the result of an advanced mathematical formula or just ha pure guess done on your favorite team is very different. Since the computer power in recent years has greatly improved the number of mathematical approaches has increased and it is especially the computational heavy models that have increased in number. In this thesis the Pi-rating system is used it gives each team a home and away rating that describe how good/bad they are compared to the average competing team. As an extension of the original Pi-rating model, in this thesis time series analysis is used to predict future values of the teams rating, three different methods are tested and they are simple moving average, simple exponential smoothing and an ARIMA-model. A solution to how new teams that did not play in the league last year should be handled is also suggested. Finally a breath discussion and test of different investment methods that can be applied on the final model to be used on the sport betting market. The results show that the greatest returns on the calibration data is achieved when Kelly’s formula is used as an investment method on an ARIMA(0,1,1)-model, but when this strategy is used outside calibration data, the result shows a very low profit and the method  fails to give a stable long term return, which from an investment point of view is not desirable. The conclusion is that this method is not in itself good enough to provide a safe return but is a good foundation that can be expanded to take more factors into account, and then hopefully give bigger and more stable winnings.

This study examined temporal patterns in collective employee turnover over a 75 month interval. Time series models were fit to subgroups of functional and dysfunctional turnover. Dysfunctional turnover was defined as voluntary separation among high and average performers and functional turnover was defined as voluntary separation of low performers. Results provided support for the hypothesis that temporal patterns of functional and dysfunctional turnover differ. Patterns among high and average performers were similar, such that employee turnover across several global regions increased during or near July. In contrast, employee turnover among low performers tended to spike during or soon after October. Forecast (prediction) accuracy of turnover differed across groups based on individual performance level. Specifically, turnover among low and average performers was forecast with greater accuracy than overall aggregated turnover or turnover among high performers, the latter being the most difficult to forecast. After time-dependent variation (autocorrelation) was removed from global turnover among high, average, and low performers, these series were cross-correlated with similarly cleaned organizational performance outcomes (i.e., net sales, operating income, diluted net earnings per share). Results from these analyses indicated that organizational performance had a lagged negative relationship with turnover among high performers. The dynamic nature of the turnover and performance variables examined underscores the importance of considering employee turnover as a continuous process. As such, employee turnover should be proactively managed over time.

Short-term traffic flow forecasting is a critical function in advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). Accurate forecasting results are useful to indicate future traffic conditions and assist traffic managers in seeking solutions to congestion problems on urban freeways and surface streets. There is new research interest in short-term traffic flow forecasting due to recent developments in ITS technologies. Previous research involves technologies in multiple areas, and a significant number of forecasting methods exist in literature. However, forecasting reliability is not properly addressed in existing studies. Most forecasting methods only focus on the expected value of traffic flow, assuming constant variance when perform forecasting. This method does not consider the volatility nature of traffic flow data. This paper demonstrated that the variance part of traffic flow data is not constant, and dependency exists. A volatility model studies the dependency among the variance part of traffic flow data and provides a prediction range to indicate the reliability of traffic flow forecasting. We proposed an ARIMA-GARCH (Autoregressive Integrated Moving Average- AutoRegressive Conditional Heteroskedasticity) model to study the volatile nature of traffic flow data. Another problem of existing studies is that most methods have limited forecasting abilities when there is missing data in historical or current traffic flow data. We developed a General Regression Neural Network(GRNN) based multivariate forecasting method to deal with this issue. This method uses upstream information to predict traffic flow at the studied site. The study results indicate that the ARIMA-GARCH model outperforms other methods in non-missing data situations, while the GRNN model performs better in missing data situations.

The purpose of this diploma thesis is compare selected prediction models. These models are used for analysis of the South Moravian Region unemployment and then compared with unemployment development in the Czech Republic. This thesis is separated to two parts: theoretical and practical. The theoretical part is used to describe selected prediction models, psychological effects of unemployment on human being and impact of unemployment on the society. The second part is focused on the analysis of the South Moravian Region. Prediction models such as trend lines, moving averages, ARIMA method and neural network are used and compared which each other. All predictions are based on data from the Czech Statistical Office and covers period between year 1993 and 2012. This data is measured with periodicity of three months.

Made available in DSpace on 2010-04-20T20:08:12Z (GMT). No. of bitstreams: 0 Previous issue date: 1993-05-14T00:00:00Z / Este trabalho compara procedimentos de previsão de preços de commodities, utilizados de maneira empírica pelos analistas de mercado, com os procedimentos fornecidos pela Análise de Séries Temporais. Aplicamos os métodos de previsão utilizando as Médias Móveis, os métodos baseados em Alisamentos exponenciais e principalmente os modelos ARIMA de Box-Jenkins. Estes últimos são, em geral, generalizações dos primeiros, com a vantagem de utilizar os instrumentos estatísticos de medidas das incertezas, como o desvio-padrão e os intervalos de confiança para as previsões.