No description available.

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.

No description available.

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.

No description available.

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%.

The ability to navigate through a continuously changing business landscape has been a success factor for Scania to stay a competitive business, when the landscape continues to change. Digitalization has enabled data to be collected from various sources and the ability to embrace the possibilities that come with it and turn it into an advantage is crucial to make sure that Scania is driving the changing industry. Today, Scania is good at collecting and analyzing data but there is room for improvements when it comes to utilizing the data to create data-driven decision-making. This study aims to investigate the possibility of learning more about the users driving behavior through data-driven driving evaluation. This is done with a machine learning approach where a CNN-GRU neural network with an XGBoost classifier is created to classify triaxial acceleration data into normal or aggressive driving behavior. The findings show that this model architecture has a classification accuracy of 87.80 % and the result is discussed with respect to method implementation, quality of data, hyperparameter tuning, and future studies.

<p dir="ltr">Data is the key component of every machine-learning algorithm. Without sufficient quantities of quality data, the vast majority of machine learning algorithms fail to perform. Acquiring the data necessary to feed algorithms, however, is a universal challenge. Recently, synthetic data production methods have become increasingly relevant as a method of ad-dressing a variety of data issues. Synthetic data allows researchers to produce supplemental data from an existing dataset. Furthermore, synthetic data anonymizes data without losing functionality. To advance the field of synthetic data production, however, measuring the quality of produced synthetic data is an essential step. Although there are existing methods for evaluating synthetic data quality, the methods tend to address finite aspects of the data quality. Furthermore, synthetic data evaluation from one study to another varies immensely adding further challenge to the quality comparison process. Finally, al-though tools exist to automatically tune hyperparameters, the tools fixate on traditional machine learning applications. Thus, identifying ideal hyperparameters for individual syn-thetic data generation use cases is also an ongoing challenge.</p>