UC Riverside

Many time series data mining algorithms are designed to be intuitive for domain experts, often including case studies and benchmark results. However, it is challenging to create truly domain-agnostic algorithms due to unique domain-specific invariances. In these works, we prioritize and leverage user input and interpretability over complexity. We empirically demonstrate that many time series data mining tasks are still a searchable space, allowing for interpretable methodologies. We adopt a ``Soup-to-Nuts" design, envisioning how the method fits into a user's problem, from accepting their real-valued time series dataset to providing a direct answer to the data mining task. With these three characteristics in mind, we propose methods that are user-friendly, not requiring the user to have extensive experience with time series data mining methods and find the results of our methods actionable. In the first work, we target a problem setting of long time series. Many time series datasets have been cleaned and preprocessed to be of uniform length and have guarantees that the time series contains some semantic data the dataset advertises. We relax these two constraints, considering signals without any assumptions about their contents. In the second work, we propose a time series anomaly explanation method and format. With the increasing relevance of time series anomaly detection, we formulate an intuitive and actionable anomaly explanation method, addressing the often complicated and unclear actionability of existing explanations. In the third work, we propose a method for time series clustering, leveraging feature sets for interpretability and user input for customizability, guiding our methodology to make better-suited and more intelligible decisions for users based on their specific needs.