Abstract: Methods and systems for time series forecasting using ensemble machine learning are disclosed. A computer system (distributed or otherwise) can instantiate, train, and use a plurality of machine learning models to generate time series forecasts. These can include both different types of machine learning models, as well as similar machine learning models that have different configurations. Embodiments of the present disclosure can use a novel modification of k-folds cross validation techniques that preserves the order of temporal data. Time series data can be partitioned into segments and folds and used to train and test the plurality of machine learning models. Forecasts produced by the trained machine learning models, along with historical time series data (or “actuals”) can be used to train an ensemble machine learning model to produce an ensemble forecast based on forecasts generated by the trained machine learning models.

Abstract: Various embodiments can reduce variance in a target metric (e.g., experiment outcome). Embodiments can use historical pre-experiment outcomes to predict a metric (forecasts) that is expected for a future measurement time. The forecasts can then be used as a covariate to reduce the variance of the target metric. When predicting forecasts, various embodiments can use an automation pipeline that can generate better and quicker forecasts. When there are multiple covariates that may be considered to reduce variance in the target metric, various embodiments can use a closed from solution for determining optimal coefficient of each covariate.