Bitcoin has since its inception in 2009 seen its market capitalisation rise to a staggering 846 billion US Dollars making it the world’s leading cryptocurrency. This has attracted financial analysts as well as researchers to experiment with different models with the aim of developing one capable of predicting Bitcoin closing prices. The aim of this thesis was to examine how well the LSTM and the SVR models performed in predicting Bitcoin closing prices. As a measure of performance, the RMSE, NRMSE and MAPE were used as well as the Random walk without drift as a benchmark to further contextualise the performance of both models. The empirical results show that the Random walk without drift yielded the best results for both the RMSE and NRMSE scoring 1624.638 and 0.02525, respectively while the LSTM outperformed both the Random Walk without drift and the SVR model in terms of the MAPE scoring 0.0272 against 0.0274 for both the Random walk without drift and SVR, respectively. Given the performance of the Random Walk against both models, it cannot be inferred that the LSTM and SVR models yielded statistically significant predictions. / <p>Aaron Green</p>

This thesis looks at how accurately one can model accents in jazz solos, more specifically the sound level. Further understanding the structure of jazz solos can give a way of pedagogically presenting differences within music styles and even between performers. Some studies have tried to model perceived accents in different music styles. In other words, model how listeners perceive some tones as somehow accentuated and more important than others. Other studies have looked at how the sound level correlates to other attributes of the tone. But to our knowledge, no other studies have been made modeling actual accents within jazz solos, nor have other studies had such a big amount of training data. The training data used is a set of 456 solos from the Weimar Jazz Database. This is a database containing tone data and metadata from monophonic solos performed with multiple instruments. The features used for the training algorithms are features obtained from the software Director Musices created at the Royal Institute of Technology in Sweden; features obtained from the software "melfeature" created at the University of Music Franz Liszt Weimar in Germany; and features built upon tone data or solo metadata from the Weimar Jazz Database. A comparison between these is made. Three learning algorithms are used, Multiple Linear Regression (MLR), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost). The first two are simpler regression models while the last is an award-winning tree boosting algorithm. The tests resulted in eXtreme Gradient Boosting (XGBoost) having the highest accuracy when combining all the available features minus some features that were removed since they did not improve the accuracy. The accuracy was around 27% with a high standard deviation. This tells that there was quite some difference when predicting the different solos, some had an accuracy of about 67% while others did not predict one tone correctly in the entire solo. But as a general model, the accuracy is too low for actual practical use. Either the methods were not the optimal ones or jazz solos differ too much to find a general pattern. / Detta examensarbete undersöker hur väl man kan modellera accenter i jazz-solos, mer specifikt ljudnivån. En bredare förståelse för strukturen i jazzsolos kan ge ett sätt att pedagogiskt presentera skillnaderna mellan olika musikstilar och även mellan olika artister. Andra studier har försökt modellera uppfattade accenter inom olika musik-stilar. Det vill säga, modellera hur åhörare upplever vissa toner som accentuerade och viktigare än andra. Andra studier har undersökt hur ljudnivån är korrelerad till andra attribut hos tonen. Men såvitt vi vet, så finns det inga andra studier som modellerar faktiska accenter inom jazzsolos, eller som haft samma stora mängd träningsdata. Träningsdatan som använts är ett set av 456 solos tagna från Weimar Jazz Database. Databasen innehåller data på toner och metadata från monofoniska solos genomförda med olika instrument. Särdragen som använts för tränings-algoritmerna är särdrag erhållna från mjukvaran Director Musices skapad på Kungliga Tekniska Högskolan i Sverige; särdrag erhållna från mjukvaran ”melfeature” skapad på University of Music Franz Liszt Weimar i Tyskland; och särdrag skapade utifrån datat i Weimar Jazz Database. En jämförelse mellan dessa har också gjorts. Tre inlärningsalgoritmer har använts, Multiple Linear Regression (MLR), Support Vector Regression (SVR), och eXtreme Gradient Boosting (XGBoost). De första två är enklare regressionsalgoritmer, medan den senare är en prisbelönt trädförstärkningsalgoritm. Testen resulterade i att eXtreme Gradient Boosting (XGBoost) skapade en modell med högst noggrannhet givet alla tillgängliga särdrag som träningsdata minus vissa särdrag som tagits bort då de inte förbättrar noggrannheten. Den erhållna noggrannheten låg på runt 27% med en hög standardavvikelse. Detta pekar på att det finns stora skillnader mellan att förutsäga ljudnivån mellan de olika solin. Vissa solin gav en noggrannhet på runt 67% medan andra erhöll inte en endaste ljudnivå korrekt i hela solot. Men som en generell modell är noggrannheten för låg för att användas i praktiken. Antingen är de valda metoderna inte de bästa, eller så är jazzsolin för olika för att hitta ett generellt mönster som går att förutsäga.

Accurate demand forecasting is a key capability for a manufacturing organization, more so, a semiconductor manufacturer. Many crucial decisions are based on demand forecasts. The semiconductor industry is characterized by very short product lifecycles (10 to 24 months) and extremely uncertain demand. The pace at which both the manufacturing technology and the product design changes, induce change in manufacturing throughput and potential demand. Well known methods like exponential smoothing, moving average, weighted moving average, ARMA, ARIMA, econometric methods and neural networks have been used in industry with varying degrees of success. We propose a novel forecasting technique which is based on Support Vector Regression (SVR). Specifically, we formulate ν-SVR models for semiconductor product demand data. We propose a 3-phased input vector modeling approach to comprehend demand characteristics learnt while building a standard ARIMA model on the data. Forecasting Experimentations are done for different semiconductor product demand data like 32 & 64 bit CPU products, 32bit Micro controller units, DSP for cellular products, NAND and NOR Flash Products. Demand data was provided by SRC(Semiconductor Research Consortium) Member Companies. Demand data was actual sales recorded at every month. Model performance is judged based on different performance metrics used in extant literature. Results of experimentation show that compared to other demand forecasting techniques ν-SVR can significantly reduce both mean absolute percentage errors and normalized mean-squared errors of forecasts. ν-SVR with our 3-phased input vector modeling approach performs better than standard ARIMA and simple ν-SVR models in most of the cases.

Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised classification tasks. In this thesis, we propose new algorithms for solving semi-supervised binary classification problem using GP regression (GPR) models. The algorithms are closely related to semi-supervised classification based on support vector regression (SVR) and maximum margin clustering. The proposed algorithms are simple and easy to implement. Also, the hyper-parameters are estimated without resorting to expensive cross-validation technique. The algorithm based on sparse GPR model gives a sparse solution directly unlike the SVR based algorithm. Use of sparse GPR model helps in making the proposed algorithm scalable. The results of experiments on synthetic and real-world datasets demonstrate the efficacy of proposed sparse GP based algorithm for semi-supervised classification.