Time series data mining is one branch of data mining. Time series analysis and prediction have always played an important role in human activities and natural sciences. A Pseudo-Periodical time series has a complex structure, with fluctuations and frequencies of the times series changing over time. Currently, Pseudo-Periodicity of time series brings new properties and challenges to time series analysis and prediction. This thesis proposes two original computational approaches for time series analysis and prediction: Moving Average of nth-order Difference (MANoD) and Series Features Extraction (SFE). Based on data-driven methods, the two original approaches open new insights in time series analysis and prediction contributing with new feature detection techniques. The proposed algorithms can reveal hidden patterns based on the characteristics of time series, and they can be applied for predicting forthcoming events. This thesis also presents the evaluation results of proposed algorithms on various pseudo-periodical time series, and compares the predicting results with classical time series prediction methods. The results of the original approaches applied to real world and synthetic time series are very good and show that the contributions open promising research directions.

This dissertation will focus on the forecasting and classification of time series. Specifically, the forecasting problem will focus on imbalanced time series (ITS) which contain a mix of a mix of low probability extreme observations and high probability normal observations. Two approaches are proposed to improve the forecasting of ITS. In the first approach proposed in chapter 2, an ITS will be modelled as a composition of normal and extreme observations, the input predictor variables and the associated forecast output will be combined into moving blocks, and the blocks will be categorized as extreme event (EE) or normal event (NE) blocks. Imbalance will be decreased by oversampling the minority EE blocks and undersampling the majority NE blocks using modifications of block bootstrapping and synthetic minority oversampling technique (SMOTE). Convolution neural networks (CNNs) and long-short term memory (LSTMs) will be selected for forecast modelling. In the second approach described in chapter 3, which focuses on improving the forecasting accuracies LSTM models, a training strategy called Circular-Shift Circular Epoch Training (CSET), is proposed to preserve the natural ordering of observations in epochs during training without any attempt to balance the extreme and normal observations. The strategy will be universal because it could be applied to train LSTMs to forecast events in normal time series or in imbalanced time series in exactly the same manner. The CSET strategy will be formulated for both univariate and multivariate time series forecasting. The classification problem will focus on the classification event-related potential neural time series by exploiting information offered by the cone of influence (COI) of the continuous wavelet transform (CWT). The COI is a boundary that is superimposed on the wavelet scalogram to delineate the coefficients that are accurate from those that are inaccurate due to edge effects. The features derived from the inaccurate coefficients are, therefore, unreliable. It is hypothesized that the classifier performance would improve if unreliable features, which are outside the COI, are zeroed out, and the performance would improve even further if those features are cropped out completely. Two CNN multidomain models will be introduced to fuse the multichannel Z-scalograms and the V-scalograms. In the first multidomain model, referred to as the Z-CuboidNet, the input to the CNN will be generated by fusing the Z-scalograms of the multichannel ERPs into a frequency-time-spatial cuboid. In the second multidomain model, referred to as the V-MatrixNet, the CNN input will be formed by fusing the frequency-time vectors of the V-scalograms of the multichannel ERPs into a frequency-time-spatial matrix.

This work adopts a feminist perspective to analyse and compare the patriarchal oppression and objectification of humans and robots in the Foundation universe. It analyses the relationships between males and females, between humans from different social and cultural backgrounds, and between humans and robots. The study attempts to capture changes that span a long period and multiple locations, exploring how those changes are triggered by different forms of patriarchal oppression and objectification. This essay concludes that various forms of patriarchal oppression and objectification exist in the beginning but start slowly disintegrating as humanity, guided by robots, move towards a greater goal of establishing a better society.

The advent of music streaming has made it increasingly important for actors in the music industry to understand if tracks are going to succeed or not. This study investigates if it is possible to accurately classify the growth of the listener base of a music track based on multivariate time series with listener behavior data. 18 popular time series classification algorithms were used to build predictive models which were evaluated in a 10-fold cross-validation. We also examined the algorithms’ potential to deliver business value for a record label. Lastly, the possibilities and challenges of applying a data-driven business model in the music industry were investigated by performing a comparative analysis of a modern and traditional record label. Six algorithms were found to significantly outperform the baseline. Two algorithms based on convolutional kernels, RR and AMini, were found to present the biggest business value because of their accuracy and low time complexity. While it may be necessary for record labels to adopt data-driven business models to flourish in the modern market, there are difficulties regarding the competitiveness of digital solutions and complications in moving the focus from networking to developing technology. / Spridningen av musiktjänster har gjort det alltmer viktigt för aktörer i musikbranschen att förstå vilka låtar som kommer att lyckas och inte. Denna studie undersöker om det är möjligt att klassificera tillväxten av en låts lyssnarantal baserat på multivariata tidsserier innehållandes data om lyssnarbeteende. 18 populära algoritmer för tidsserieklassificering användes för att bygga prediktiva modeller som utvärderades med 10-delad korsvalidering. Vi undersökte sedan algoritmernas potential att skapa affärsvärde för ett skivbolag. Slutligen studerades möjligheter och utmaningar som datadrivna affärsmodeller presenterar i denna bransch genom en komparativ analys av ett modernt och traditionellt skivbolag. Sex algoritmer visade sig signifikant överträffa en baslinjeklassificerare. Vi fann att två algoritmer baserade på faltningskärnor, RR och AMini, kunde skapa störst affärsvärde på grund av deras noggrannhet samt låga tidskomplexitet. Det verkar vara nödvändigt för skivbolag att anamma datadrivna affärsmodeller för att frodas i den moderna marknaden, men det finns svårigheter som måste beaktas vad gäller konkurrenskraften för digitala lösningar samt förflyttandet av fokuset från nätverksbyggande till teknologiutveckling.

La telenovela es el producto de mayor consumo en Latinoamérica. En nuestro país, representa el 13,5%1 de la oferta televisiva. La hibridación de los géneros y formatos hacen que la telenovela tenga nuevos rasgos particulares. “Al fondo hay sitio” (AFHS) es producto de esta hibridación, desde su estructura narrativa hasta el contenido de la misma. En este sentido, el número de episodios no responde al formato clásico de telenovela latina, sino, a la combinación de telenovela con sitcom y soap opera, en tanto que el número de episodios sobrepasa el promedio de 120 capítulos (que es el estándar en Latinoamérica), pues, hasta la fecha, ya han sobrepasado los 700 capítulos emitidos2 . Hay que añadir que AFHS responde más a la estructura del sitcom o del soap opera dado que el relato se prolonga en temporadas. Asimismo, encontramos que hay personajes cuyo perfil dramático “no evoluciona” ante giros dramáticos que deberían cambiar su forma de ser actuar, etc. Ello no sucede y el personaje se queda en el mismo statu quo. También, AFHS apela a las identidades culturales del público, a través de la caracterización de sus personajes o de los hechos narrados. Este fenómeno que se está creando en nuestro país es lo que se desea investigar, desde la perspectiva de las manifestaciones culturales (la identidad cultural) representadas en los personajes y las situaciones dramáticas del relato (AFHS).

En la actualidad, los canales de televisión se han visto inmersos en una vorágine de la producción, específicamente en la producción de series televisivas.Es por esto la necesidad de crear nuevos contenidos, ya sea a partir de una historia original o -como se ha visto en los últimos años- contenido basado en historias clásicas o preexistentes con el fin de reinventarlas y modernizarlas. No son pocos los ejemplos que podemos encontrar en esta lista, dos de las más recientes producciones que tienen como base un cuento clásico son la serieOne Upon a Time (2011), y V (2009) quienes fueron inspiradas en Blanca Nieves y los Siete Enanitos y la serie ladécada de los ochenta:V, Invasión Extraterrestre,respectivamente, además de una serie de películas como Supermán (2012), The Amazing Spiderman (2012), La Chica de la Capa Roja, La Bella y la Bestia (2014), Encantada (2011), Maléfica(2014), Capitán America (2011), Hércules (2014)y una lista interminable que si bien es cierto solo algunas de ellas están inspiradas en historias clásicas -sea del comic o cuentos infantiles. Lo innegable es que son la evidencia de que lo clásico está de vuelta y ha invadido la televisión, pero esta vez con un nuevo enfoque, más fresco, sin estereotipos y utilizando nuevas herramientas pensando en atraer a un nuevo público joven.

As Next Generation (NextG) networks become more complex, it has become increasingly necessary to utilize more advanced algorithms to enhance the robustness, autonomy, and reliability of existing wireless infrastructure. One such algorithm is network traffic prediction, playing a crucial role in the efficient operation of real-time and near-real-time network management. The contributions of this thesis are twofold. The first introduces a novel cluster-train-predict framework that leverages domain knowledge to identify unique timeseries sub-behaviors within aggregates of network data. This method produces distributions that are more robust towards changes in the spatio-temporal environment. The ensemble of time-series prediction models trained on these distributions posses a greater affinity towards accurate network prediction, selectively employing learned behaviors to handle sources of time-series data without any prior knowledge of it. This approach tends to improve the ability to accurately forecast network traffic volumes. Secondly, this thesis explains the development and implementation of a modular data pipeline to support the cluster-train-predict framework under a variety of conditions. This setup promotes repeatable and comparable results, facilitating rapid iteration and experimentation on current and future research. The results of this thesis surpass traditional approaches in [1] by up to 60%. Furthermore, the effectiveness of this framework is also validated using two additional time-series datasets [2] and [3], demonstrating the ability of this approach to generalize towards other time-series data and machine learning applications in uncertain environments. / Master of Science / As Next Generation (NextG) networks become more complex, it has become increasingly necessary to utilize more advanced algorithms to enhance the robustness, autonomy, and reliability of in-use wireless infrastructure where network traffic prediction plays a crucial role in the efficient operation of real-time and near real-time network management. The contributions of this thesis are twofold. The first explores a novel cluster-train-predict framework that uses an unsupervised learning approach, specifically time-series K-means clustering, to group similar time-series data. In doing so, this approach identifies unique time-series behaviors within network provider data. Since this approach aims to reduce the variance within each aggregate, models can specialize towards particular network behaviors, becoming better suited for a wider variety of network trends during prediction. Because this framework assigns data to each cluster based on these groupings, the framework can adapt towards changes in network infrastructure or underlying shifts in its environment to forecast with a greater degree of certainty and explainability. This framework can even generalize towards out-of-distribution cases where it has no prior knowledge of a source of time-series data outperforming [1] by up to 60%. This approach tends to improve the ability to accurately forecast network traffic volumes. Secondly, this thesis explains the development and implementation of a modular data pipeline to support the cluster-train-predict framework under a variety of conditions with repeatable and comparable results, facilitating rapid iteration and experimentation on current and future research. The results of the framework are also corroborated on two, additional time-series datasets [2] and [3], demonstrating the ability of this approach to generalize towards time-series data, where this framework can also be applied to other machine learning applications in uncertain environments.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第25092号 / 理博第4999号 / 新制||理||1714(附属図書館) / 京都大学大学院理学研究科数学・数理解析専攻 / (主査)教授 池田 保, 教授 市野 篤史, 准教授 伊藤 哲史 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Medan normen i tidsserieklassificering (TSC) har varit att förbättra noggrannheten, har nya modeller med fokus på effektivitet nyligen fått uppmärksamhet. I synnerhet modeller som kallas ROCKET"(RandOm Convolutional KErnel Transform), som fungerar genom att slumpmässigt generera ett stort antal kärnor som används som funktionsextraktorer för att träna en enkel åsklassificerare, kan prestera lika bra som andra toppmoderna algoritmer, samtidigt som de har en betydande ökning i effektivitet. Även om ROCKET-modeller ursprungligen designades för Univariate Time Series (UTS), som definieras av en enda kanal eller sekvens, har dessa klassificerare också visat utmärkta resultat när de testats på Multivariate Time Series (MTS), där egenskaperna för tidsserien är spridda över flera kanaler. Därför är det av vetenskapligt intresse att utforska dessa modeller för att bedöma deras övergripande prestanda och om effektiviteten kan förbättras ytterligare. Nyligen genomförda studier presenterar en ny algoritm som kallas Sequential Feature Detachment (SFD) som, förutom ROCKET, avsevärt kan minska storleken på modellerna samtidigt som noggrannheten ökar något genom en sekventiell funktionsvalsteknik. Trots dessa anmärkningsvärda resultat var experimenten som ledde till slutsatserna begränsade till användningen av UTS, vilket lämnade utrymme för utforskningen av denna algoritm på MTS. Följaktligen undersöker denna studie hur man kan utnyttja ROCKET-algoritmer och SFD för att förbättra MTS-klassificeringsuppgifter vad gäller både effektivitet och noggrannhet, samtidigt som god tolkningsbarhet bibehålls som en begränsning. För att uppnå detta genomförs experiment på flera University of East Anglia (UEA) MTS-datauppsättningar, testar modellensembler, grupperar kanaler baserat på förutsägbarhet och undersöker kanalrelevanser tillsammans med SFD. Resultaten visar hur modellanpassning inte är en metod som kan öka noggrannheten i testuppsättningarna och hur förutsägbarheten för enskilda kanaler inte bibehålls längs datapartitioner. Det visas dock hur användning av SFD med MiniROCKET, en variant av ROCKET som inkluderar slumpmässiga kanalkombinationer, inte bara förbättrar klassificeringsresultaten, utan också ger ett statistiskt signifikant kanalrelevansmått. / While the norm in Time Series Classification (TSC) has been to improve accuracy, new models focusing on efficiency have recently been attracting attention. In particular, models known as ”ROCKET” (RandOm Convolutional KErnel Transform), which work by randomly generating a large number of kernels used as feature extractors to train a simple ridge classifier, can yield results as good as other state-of-the-art algorithms while presenting a significant increase in efficiency. Although ROCKET models were originally designed for Univariate Time Series (UTS), which are defined by a single channel or sequence, these classifiers have also shown excellent results when tested on Multivariate Time Series (MTS), where the characteristics of the time series are spread across multiple channels. Therefore, it is of scientific interest to explore these models to assess their overall performance and whether efficiency can be further improved. Recent studies present a novel algorithm named Sequential Feature Detachment (SFD) which, on top of ROCKET, can significantly reduce the model size while slightly increasing accuracy through a sequential feature selection technique. Despite these remarkable results, the experiments leading to the conclusions were limited to the use of UTS, leaving room for the exploration of this algorithm on MTS. Consequently, this thesis evaluates different strategies to implement ROCKET and SFD algorithms for MTS classification tasks, focusing not only on improving efficiency and accuracy, but also on adding interpretability to the classifier. To achieve this, experiments were conducted by testing model ensembles, grouping channels based on predictability, and examining channel relevances alongside SFD. The University of East Anglia (UEA) MTS archive was used to evaluate the resulting models, as it is common with TSC algorithms. The results demonstrate that model ensembling does not increase accuracy in the test sets and that the predictability of individual channels is not maintained across dataset splits. However, the study shows that using SFD with MiniROCKET, a variant of ROCKET that includes random channel combinations, not only can improve classification results but also provide a statistically significant channel relevance measure.

Anomalies are rare in nature. This rarity makes it difficult for models to provide accurate and reliable predictions. Deep learning models typically excel at identifying underlying patterns from abundant data through supervised learning mechanisms but struggle with anomalies due to their limited representation. This results in a significant portion of errors arising from these rare and poorly represented events. Here, we present various methods and frameworks to develop the specialized ability of models to better detect and predict anomalies. Additionally, we improve the interpretability of these models by enhancing their anomaly awareness, leading to stronger performance on real-world datasets that often contain such anomalies. Because our models dynamically adapt to the significance of anomalies, they benefit from increased accuracy and prioritization of rare events in predictions. We demonstrate such capabilities on real-world datasets across multiple domains. Our results show that this framework enhances accuracy and interpretability, improving upon existing methods in anomaly prediction tasks.