Time series often present gaps in the data. This phenomenon, also called missing values, is so prevalent that a cottage industry of missing-value imputation algorithms exists, each with different capabilities and efficacy/efficiency tradeoffs. So far, however, there has been no way to accurately select the most appropriate approach among all algorithms, given a new time series requiring imputation. In this paper, we introduce a new configuration-free system, A-DARTS (for Automated DAta Repair in Time Series), to automatically select the best imputation technique for a given faulty time series. A-DARTS's recommendation engine is trained via an iterative process that carefully learns the behavior of imputation algorithms using an extensive dataset of time series that we curated. The selection process is made efficient by several new pruning techniques particularly adjusted to time series data. Applications that manipulate time series can now easily embed A-DARTS's recommendation engine and impute data on the fly. Our experiments show that our system picks, on average, the best imputation algorithm 20% more frequently than the best-in-class AutoML technique. Moreover, it produces stable recommendations across datasets by incurring 2.5x less error variance, eliminating the stability issue observed in all state-of-the-art methods we tested.

In recent years, Artificial Intelligence (AI) has seen a remarkable expansion. Traditionally, applications primarily relied on edge computing, closer to data sources. However, today edge computing approaches are outstripped by modern AI demands. In this paper, we introduce a novel framework together with an online decision algorithm based on multi-armed contextual bandits (DroneBandit), for the dynamic allocation of inference tasks between edge and cloud, to increase inference performance. Our test environment consists of a resource-constrained nanodrone equipped with a custom DNN for obstacle detection, able to achieve 80% accuracy and 71% F2-score. DroneBandit runs on the drone and chooses the optimal cutting point for each iteration on the fly. The decision is based on predicted back-end delays (i.e. data transfer and inference time on the cloud) and observed front-end delays (i.e. inference time on the edge). DroneBandit achieves 83% accuracy and 89% Top3 accuracy in predicting the ideal cutting point on simulations. Our framework demonstrates enhanced task allocation adaptability, enabling efficient computation offloading and edge computing reliance in varying network conditions. Our experiments, conducted on a nanodrone with an ARM CPU and GAP8 RISC-V accelerator, incorporate quantization and optimization, showcasing efficient obstacle detection in dynamic scenarios.