Good generative models for short time series data exist and have been applied for both data augmentation and privacy protection purposes in the past. A common theme for existing generative models is that they all use a recurrent neural network (RNN) architecture, which makes the models limited regarding the length of the sequences. In real world problems, we might have to deal with data containing longer sequences, and it is such data we in this thesis attempt to synthesize. By combining the recently successful TimeGAN framework with a temporal convolutional network component architecture, we generate synthetic sequential data for two toy data sets: sequential MNIST and multivariate sine waves. The results strongly indicate, although relying solely on a visual inspection, that the model manage to capture long temporal dynamics over time and also relations between different features for the multivariate sine waves data set. In order to make our model applicable for real world data sets, we suggest two improvements. Firstly, the validation of the generated data should not only rely on visual inspection, but also ensure that the synthetic data has the same statistical distribution. Secondly, depending on the task, model refinements such that the synthetic samples look even more realistic should be made.

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Over the past decade, the field of affective computing has received increasing attention. With advancements in machine learning, a wide range of methodologies have been developed to better understand human emotions. However, one of the major challenges in this field is accurately modeling emotions on a set of continuous dimensions, such as arousal and valence. This type of modeling is essential to represent complex and subtle emotions, and to capture the full spectrum of human emotional experiences. Additionally, predicting changes in emotions across time series adds another layer of complexity, as emotions can shift continuously. Our work addresses these challenges using a dataset that includes natural and spontaneous emotions from diverse individuals. We extract multiple features from different modalities, including audio, video, and text, and use them to predict emotions across three axes: arousal, valence, and liking. To achieve this, we employ deep features and multiple fusion techniques to combine the modalities. Our results demonstrate that temporal convolutional networks outperform long short-term memory models in multimodal emotion prediction. Overall, our research contributes to advancing the field of affective computing by developing more accurate and comprehensive methods for modeling and predicting human emotions.

A better understanding of the traffic flow in a city helps to smooth transport resulting in a better street environment, affecting not only road users and people in proximity. Good predictions of the flow of traffic helps to control and further develop the road network in order to avoid congestion and unneccessary time spent while traveling. This study investigates three different machine learning models with the purpose of predicting traffic flow on different road types inurban Stockholm using loop sensor data between 2013 and 2023. The models used was Long short term memory (LSTM), Temporal convolutional network (TCN) and a hybrid model of LSTM and TCN. The results from the hybrid model indicates a slightly better mean absolute error than TCN suggesting that a hybrid model might be advantagous when predicting traffic flow using loop sensor data. LSTM struggled to capture the complexity of the data and was unable to provide a proper prediction as a result. TCN produced a mean absolute error slightly bigger than the hybrid model and was to an extent able to capture the trends of the traffic flow, but struggled with capturing the scale of the traffic flow suggesting the need for further data preprocessing. Furthermore, this study suggests that the loop sensor data was able to act as a foundation for predicting the traffic flow using machine learning methods. However, it suggest that improvements to the data itself such as incorporating more related parameters might be advantageous to further improve traffic flow prediction.