E-Divisive with Medians (EDM) 
E-Divisive with Medians (EDM) – employs energy statistics to detect divergence in mean. Note that EDM can also be used detect change in distribution in a given time series. EDM uses robust statistical metrics, viz., median, and estimates the statistical significance of a breakout through a permutation test. In addition, EDM is non-parametric. This is important since the distribution of production data seldom (if at all) follows the commonly assumed normal distribution or any other widely accepted model. …
Reinforced Evolutionary Neural Architecture Search (RENAS)
Neural architecture search (NAS) is an important task in network design, but it remains challenging due to high computational consumption in most methods and low stability in evolution algorithm (EA) based NAS. In this paper, we propose the Reinforced Evolutionary Neural Architecture Search (RENAS), an evolutionary method with reinforced mutation for NAS to address these two issues. Specifically, we integrate reinforced mutation into an EA based NAS method by adopting a mutation controller to learn the effects of slight modifications and make mutation actions. For this reason, the proposed method is more like the process of model design by human experts than typical RL-based NAS methods that construct networks sequentially. Furthermore, as the models are trained by fine-tuning rather than from scratch in model evaluation, the cell-wise search process becomes much more efficient and only takes less than 1.5 days using 4 GPUs (Titan xp). Experimental results demonstrate the effectiveness and efficiency of our method. Moreover, the architecture searched on CIFAR-10 sets a new state-of-the-art on ImageNet in the mobile setting (top-1/5 accuracy = 75.7%/92.6%). …
Exponential Smoothing
Exponential smoothing is a technique that can be applied to time series data, either to produce smoothed data for presentation, or to make forecasts. The time series data themselves are a sequence of observations. The observed phenomenon may be an essentially random process, or it may be an orderly, but noisy, process. Whereas in the simple moving average the past observations are weighted equally, exponential smoothing assigns exponentially decreasing weights over time. Exponential smoothing is commonly applied to financial market and economic data, but it can be used with any discrete set of repeated measurements. The simplest form of exponential smoothing should be used only for data without any systematic trend or seasonal components. …
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