Algorithms support for timeseries forecasting
Autopilot trains the following six builtin algorithms with your target timeseries. Then, using a stacking ensemble method, it combines these model candidates to create an optimal forecasting model for a given objective metric.

Convolutional Neural Network  Quantile Regression (CNNQR) – CNNQR is a proprietary machine learning algorithm for forecasting timeseries using causal convolutional neural networks (CNNs). CNNQR works best with large datasets containing hundreds of timeseries.

DeepAR+ – DeepAR+ is a proprietary machine learning algorithm for forecasting timeseries using recurrent neural networks (RNNs). DeepAR+ works best with large datasets containing hundreds of feature timeseries.

Prophet – Prophet
is a popular local Bayesian structural time series model based on an additive model where nonlinear trends are fit with yearly, weekly, and daily seasonality. The Autopilot Prophet algorithm uses the Prophet class of the Python implementation of Prophet. It works best with timeseries with strong seasonal effects and several seasons of historical data. 
NonParametric Time Series (NPTS) – The NPTS proprietary algorithm is a scalable, probabilistic baseline forecaster. It predicts the future value distribution of a given timeseries by sampling from past observations. NPTS is especially useful when working with sparse or intermittent time series.

Autoregressive Integrated Moving Average (ARIMA) – ARIMA is a commonly used statistical algorithm for timeseries forecasting. The algorithm captures standard temporal structures (patterned organizations of time) in the input dataset. It is especially useful for simple datasets with under 100 time series.

Exponential Smoothing (ETS) – ETS is a commonly used statistical algorithm for timeseries forecasting. The algorithm is especially useful for simple datasets with under 100 time series, and datasets with seasonality patterns. ETS computes a weighted average over all observations in the time series dataset as its prediction, with exponentially decreasing weights over time.