## 1 Introduction  
With the advent of the \"Internet of Things\", it has become increasingly commonplace to develop real-time alerting and monitoring systems capable of mitigating the risk of mechanical failures via human intervention. Indeed, there are several challenges posed by such systems. First, the multiple time series generated in these scenarios often exhibit non-linear/non-Gaussian temporal dependencies. Next, in order for alerting mechanisms to be effective, there is a need to develop statistical methods capable of assessing the impact of exogenous interventions (alerts) in the form of spurring prompt human intervention. Finally, because there are likely thousands of such sensors involved in these types of IoT programs, any modeling paradigm must be readily scalable. In this paper, we propose a latent autoregressive Student-\(t\) process model to accomplish all three of these goals. Intervention analysis is a well-established time series approach. An integral component of a successful intervention analysis is the use of a suitable time series model to learn the pre-intervention time series behavior. Linear models have traditionally been employed for this purpose. These include Gaussian autoregressive moving average (ARIMA) models [1; 2], Gaussian dynamic linear models (DLM) [3; 4], or Bayesian structural time series (BSTS) models [5; 6]. In the Bayesian framework, the pre-intervention model is used to derive the joint posterior predictive distribution of _post-intervention_ observations. Samples from this posterior predictive distribution serve as _counterfactuals_ to the post-intervention observations. By measuring the difference between these forecast values and the observed post-intervention data, a _semi-parametric_ posterior estimate for the impact of the intervention is constructed. Due to its simplicity and versatility, this methodology described in [6] has been employed across a wide array of disciplines. For instance, [PERSON]. [7] adapted the BSTS model to evaluate the impact of rebates for turf removal on water consumption across many households. In the public health context, [PERSON]. [8] evaluated the impact of bariatric surgery (used for weight loss) on health care utilization in Germany. Another interesting example is given by [9], who used the impact framework in conjunction with a variety of climate time series to assess whether an anomalous climate change event can be credibly linked to the collapse of several Bronze age civilizations in the Mediterranean region. For intervention impact analysis to be successful, it is critical that the underlying time series model adequately captures the pre-intervention time series dynamics. Traditional linear, Gaussian models can be inadequate for capturing the dynamics of time series that exhibit complex non-linear and/or long-term dependencies, and/or non-Gaussian behavior. As a consequence, the counterfactual forecasts may be inadequate to give a useful assessment of the impact. For example, multiple time series generated by \"Internet of Things\" (IoT) sensors often exhibit nonlinear temporal dependence that cannot be easily modeled by BSTS models. Successful intervention analysis of such time series requires sophisticated models of pre-intervention data such as those described in this paper. For intervention impact analysis in multiple IoT time series, [PERSON]. [10] proposed a Gaussian process (GP) prior regression model [11] with a covariance kernel tailored for these series as the underlying predictive model. This model is effective in that it can incorporate typical time series behavior such as seasonality and local linear trends but also non-linear time trends and dependencies between the target variable and exogenous predictors. While this model was demonstrated to be effective at capturing a wide array of time series dynamics, it does not directly incorporate information from past values of the time series. In addition, the GP prior can fail for time series that exhibit a heavy-tailed behavior.