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The lumber industry in the South is an important sector, and has connections with many other key industries. The dynamics of the southern lumber market and its linkage with other related markets can be examined by the price transmissions. The first part of this study investigates vertical price transmission traced back to delivered sawlog market and stumpage market, and arrives at the conclusion that the supply chain is generally efficient with positive asymmetric transmission involved in one product. The second part explores the relationship between markets of the South and Pacific Northwest and concludes that the two markets are more balanced with each other after various demand and supply shocks with two regime switching models. This research will benefit market participants and policy makers to update their knowledge and obtain efficient information before decision making.

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Accurate prediction of stock prices plays an increasingly prominent role in the stock market where returns and risks fluctuate wildly, and both financial institutions and regulatory authorities have paid sufficient attention to it. As a method of asset allocation, stocks have always been favored by investors because of their high returns. The research on stock price prediction has never stopped. In the early days, many economists tried to predict stock prices. Later, with the in-depth research of mathematical theory and the vigorous development of computer technology, people have found that the establishment of mathematical models can be very good, such as time series model, because its model is relatively simple and the forecasting effect is better. Time series model is applied in a period of time The scope gradually expanded. However, due to the non-linearity of stock data, some machine learning methods, such as support vector machines. Later, with the development of deep learning, some such as RNN, LSTM neural Networks, they can not only process non-linear data, but also retain memory for the sequence and retain useful information, which is positive. It is required for stock data forecasting. This article introduces the theoretical knowledge of time series model and LSTM neural network, and select real stocks in the stockmarket, perform modeling analysis and predict stock prices, and then use the root mean square error to compare the prediction results of several models. Since the time series model cannot make good use of the non-linear part of the stock data, can’t perform long-term memory, and LSTM neural network makes better use of non-linear data and has better use of sequence data. Useful information in the long-term memory, which makes the root mean square error of the prediction result, the LSTM neural network needs smaller than the time series model, indicating that LSTM neural network is a better stock price forecasting method. The time series for stock prices belong to non-stationary and non-linear data, making the prediction of future price trends extremely challenging. In order to learnthe long-term dependence of stock prices, deep learning methods such as the LSTM method are used to obtain longer data dependence and overall change patterns of stocks. This thesis uses 5000 observations from S&P500 index for empirical research, and introduce benchmark models, such as ARIMA, GARCH and other research methods for comparison, to verify the effectiveness and advantages of deep learning methods.

In today's world the amount of collected data increases every day, this is a trend which is likely to continue. At the same time the potential value of the data does also increase due to the constant development and improvement of hardware and software. However, in order to gain insights, make decisions or train accurate machine learning models we want to ensure that the data we collect is of good quality. There are many definitions of data quality, in this thesis we focus on the accuracy aspect. One method which can be used to ensure accurate data is to monitor for and alert on anomalies. In this thesis we therefore suggest a method which, based on historic values, is able to detect anomalies in time series as new values arrive. The method consists of two parts, forecasting the next value in the time series using Holt-Winters method and comparing the residual to an estimated Gaussian distribution. The suggested method is evaluated in two steps. First, we evaluate the forecast accuracy for Holt-Winters method using different input sizes. In the second step we evaluate the performance of the anomaly detector when using different methods to estimate the variance of the distribution of the residuals. The results indicate that the suggested method works well most of the time for detection of point anomalies in seasonal and trending time series data. The thesis also discusses some potential next steps which are likely to further improve the performance of this method. / I dagens värld ökar mängden insamlade data för varje dag som går, detta är en trend som sannolikt kommer att fortsätta. Samtidigt ökar även det potentiella värdet av denna data tack vare ständig utveckling och förbättring utav både hårdvara och mjukvara. För att utnyttja de stora mängder insamlade data till att skapa insikter, ta beslut eller träna noggranna maskininlärningsmodeller vill vi försäkra oss om att vår data är av god kvalité. Det finns många definitioner utav datakvalité, i denna rapport fokuserar vi på noggrannhetsaspekten. En metod som kan användas för att säkerställa att data är av god kvalité är att övervaka inkommande data och larma när anomalier påträffas. Vi föreslår därför i denna rapport en metod som, baserat på historiska data, kan detektera anomalier i tidsserier när nya värden anländer. Den föreslagna metoden består utav två delar, dels att förutsäga nästa värde i tidsserien genom Holt-Winters metod samt att jämföra residualen med en estimerad normalfördelning. Vi utvärderar den föreslagna metoden i två steg. Först utvärderas noggrannheten av de, utav Holt-Winters metod, förutsagda punkterna för olika storlekar på indata. I det andra steget utvärderas prestandan av anomalidetektorn när olika metoder för att estimera variansen av residualernas distribution används. Resultaten indikerar att den föreslagna metoden i de flesta fall fungerar bra för detektering utav punktanomalier i tidsserier med en trend- och säsongskomponent. I rapporten diskuteras även möjliga åtgärder vilka sannolikt skulle förbättra prestandan hos den föreslagna metoden.

This research focuses on addressing two pertinent problems in machine learning (ML) which are (a) the supervised classification of time series and (b) the need for large amounts of labeled images for training supervised classifiers. The novel contributions are two-fold. The first problem of time series classification is addressed by proposing to transform time series into domain-specific 2D features such as scalograms and recurrence plot (RP) images. The second problem which is the need for large amounts of labeled image data, is tackled by proposing a new way of using a reinforcement learning (RL) technique as a supervised classifier by using multimodal (joint representation) scalograms and RP images. The motivation for using such domain-specific features is that they provide additional information to the ML models by capturing domain-specific features (patterns) and also help in taking advantage of state-of-the-art image classifiers for learning the patterns from these textured images. Thus, this research proposes a multimodal fusion (MMF) - deep reinforcement learning (DRL) approach as an alternative technique to traditional supervised image classifiers for the classification of time series. The proposed MMF-DRL approach produces improved accuracy over state-of-the-art supervised learning models while needing fewer training data. Results show the merit of using multiple modalities and RL in achieving improved performance than training on a single modality. Moreover, the proposed approach yields the highest accuracy of 90.20% and 89.63% respectively for two physiological time series datasets with fewer training data in contrast to the state-of-the-art supervised learning model ChronoNet which gave 87.62% and 88.02% accuracy respectively for the two datasets with more training data.

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Mechanical devices in industriy are equipped with numerous sensors to capture thehealth state of the machines. The reliability of the machine’s health system depends on thequality of sensor data. In order to predict the health state of sensors, abnormal behaviourof sensors must be detected to avoid unnecessary cost.We proposed LSTM autoencoder in which the objective is to reconstruct input time seriesand predict the next time instance based on historical data, and we evaluate anomaliesin multivariate time series via reconstructed error. We also used exponential moving averageas a preprocessing step to smooth the trend of time series to remove high frequencynoise and low frequency deviation in multivariate time series data.Our experiment results, based on different datasets of multivariate time series of gasturbines, demonstrate that the proposed model works well for injected anomalies and realworld data to detect the anomaly. The accuracy of the model under 5 percent infectedanomalies is 98.45%.

Foreign trade is increasingly becoming a powerful tool when it comes to the promotion of economic growth in modern economies. This is especially so in the face of the continued rise of globalisation. In consideration of this fact, this thesis assessed the impact of foreign trade on the growth process of Namibia’s economy for the period stretching from 1990 to 2012. This main objective was further developed into primary, theoretical and empirical objectives. In order to realise these multiple objectives, two modern econometric time series techniques were employed, namely vector autoregressive (VAR) and auto-regression distributed lag (ARDL) models. Based on these two techniques, the following procedures featured during the study: Stationary tests, error correction modelling, co-integration tests, Granger causality tests, generalised impulse response functions and generalised forecast error variance decomposition. The following constitutes the main findings arising from this study: First, the study found that there is a positive relationship among the variables that were investigated. Indeed, this positive relationship suggests that the economy of Namibia can be expanded potentially by means of foreign trade. The result is also in line with economic theory. Secondly, the empirical findings also show that export, foreign direct investment and exchange rate endogenously respond to shocks in economic growth. Thirdly, economic growth itself accounted for most of the innovations that occurred during the period under consideration concerning economic growth. Fourthly, amongst the three explanatory variables used in the model, exports and foreign direct investment contributed more towards innovations in economic growth during the forecast period. Initially, exports and foreign direct investment dominated over the forecast horizon with each contributing almost an equal share of over 5 percent after 12 quarters. Thereafter, exports’ contribution relatively exceeded that of foreign direct investment. Fifthly, it is particularly important to note that the exchange rate variable made the weakest contribution towards explaining economic growth for the forecast period of 24 quarters. In consideration of the general constraints associated with this study, the thesis puts forward a number of proposals for possible further investigation by any theorist who is keen about probing the issue that the thesis investigated. The thesis considers the following as its significant contributions to the existing literature: First, this study primarily examined the relationship between exports and economic growth. By adding the effect of foreign direct investment and exchange rate to the analysis, this study became more comprehensive. This further widens the scope for policymaking for Namibia, as well as other developing economies on a similar route. Secondly, the study employed two modern econometric time series techniques, namely VAR and ARDL models in investigating the research topic under consideration. Most of the related studies that were reviewed either utilised ordinary least squares (OLS) or VAR or ARDL approach on its own. By implication, the results obtained from this study, therefore, are from a technical point of view more robust. Thirdly, through constructive comments, this thesis made valuable contributions to the relevant empirical literature as reviewed during the course of the study. Fourthly, since this study has a focus on Namibia, it provided the opportunity for the thesis to present a comprehensive analysis on issues pertaining to Namibia specifically. Lastly, the various recommendations put forward by this thesis will assist Namibia, as well as other developing countries, on a related path when it comes to formulating policies for the promotion of exports in particular and economic growth in general. / PhD (Economics)--North-West University, Vaal Triangle Campus, 2015.

Foreign trade is increasingly becoming a powerful tool when it comes to the promotion of economic growth in modern economies. This is especially so in the face of the continued rise of globalisation. In consideration of this fact, this thesis assessed the impact of foreign trade on the growth process of Namibia’s economy for the period stretching from 1990 to 2012. This main objective was further developed into primary, theoretical and empirical objectives. In order to realise these multiple objectives, two modern econometric time series techniques were employed, namely vector autoregressive (VAR) and auto-regression distributed lag (ARDL) models. Based on these two techniques, the following procedures featured during the study: Stationary tests, error correction modelling, co-integration tests, Granger causality tests, generalised impulse response functions and generalised forecast error variance decomposition. The following constitutes the main findings arising from this study: First, the study found that there is a positive relationship among the variables that were investigated. Indeed, this positive relationship suggests that the economy of Namibia can be expanded potentially by means of foreign trade. The result is also in line with economic theory. Secondly, the empirical findings also show that export, foreign direct investment and exchange rate endogenously respond to shocks in economic growth. Thirdly, economic growth itself accounted for most of the innovations that occurred during the period under consideration concerning economic growth. Fourthly, amongst the three explanatory variables used in the model, exports and foreign direct investment contributed more towards innovations in economic growth during the forecast period. Initially, exports and foreign direct investment dominated over the forecast horizon with each contributing almost an equal share of over 5 percent after 12 quarters. Thereafter, exports’ contribution relatively exceeded that of foreign direct investment. Fifthly, it is particularly important to note that the exchange rate variable made the weakest contribution towards explaining economic growth for the forecast period of 24 quarters. In consideration of the general constraints associated with this study, the thesis puts forward a number of proposals for possible further investigation by any theorist who is keen about probing the issue that the thesis investigated. The thesis considers the following as its significant contributions to the existing literature: First, this study primarily examined the relationship between exports and economic growth. By adding the effect of foreign direct investment and exchange rate to the analysis, this study became more comprehensive. This further widens the scope for policymaking for Namibia, as well as other developing economies on a similar route. Secondly, the study employed two modern econometric time series techniques, namely VAR and ARDL models in investigating the research topic under consideration. Most of the related studies that were reviewed either utilised ordinary least squares (OLS) or VAR or ARDL approach on its own. By implication, the results obtained from this study, therefore, are from a technical point of view more robust. Thirdly, through constructive comments, this thesis made valuable contributions to the relevant empirical literature as reviewed during the course of the study. Fourthly, since this study has a focus on Namibia, it provided the opportunity for the thesis to present a comprehensive analysis on issues pertaining to Namibia specifically. Lastly, the various recommendations put forward by this thesis will assist Namibia, as well as other developing countries, on a related path when it comes to formulating policies for the promotion of exports in particular and economic growth in general. / PhD (Economics)--North-West University, Vaal Triangle Campus, 2015.