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Amazon Forecast Algorithms
An Amazon Forecast predictor uses an algorithm to train a model with your time series datasets. The trained model is then used to generate metrics and predictions.
If you are unsure of which algorithm to use to train your model, choose AutoML when creating a predictor and let Forecast train the optimal model for your datasets. Otherwise, you can manually select one of the Amazon Forecast algorithms.
Python notebooks
For a step-by-step guide on using AutoML, see Getting Started with AutoML
Built-in Forecast Algorithms
Amazon Forecast provides six built-in algorithms for you to choose from. These range from commonly used statistical algorithms like Autoregressive Integrated Moving Average (ARIMA), to complex neural network algorithms like CNN-QR and DeepAR+.
CNN-QR
arn:aws:forecast:::algorithm/CNN-QR
Amazon Forecast CNN-QR, Convolutional Neural Network - Quantile Regression, is a proprietary machine learning algorithm for forecasting time series using causal convolutional neural networks (CNNs). CNN-QR works best with large datasets containing hundreds of time series. It accepts item metadata, and is the only Forecast algorithm that accepts related time series data without future values.
DeepAR+
arn:aws:forecast:::algorithm/Deep_AR_Plus
Amazon Forecast DeepAR+ is a proprietary machine learning algorithm for forecasting time series using recurrent neural networks (RNNs). DeepAR+ works best with large datasets containing hundreds of feature time series. The algorithm accepts forward-looking related time series and item metadata.
Prophet
arn:aws:forecast:::algorithm/Prophet
Prophet is a time series forecasting algorithm based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality. It works best with time series with strong seasonal effects and several seasons of historical data.
NPTS
arn:aws:forecast:::algorithm/NPTS
The Amazon Forecast Non-Parametric Time Series (NPTS) proprietary algorithm is a scalable, probabilistic baseline forecaster. NPTS is especially useful when working with sparse or intermittent time series. Forecast provides four algorithm variants: Standard NPTS, Seasonal NPTS, Climatological Forecaster, and Seasonal Climatological Forecaster.
ARIMA
arn:aws:forecast:::algorithm/ARIMA
Autoregressive Integrated Moving Average (ARIMA) is a commonly used statistical algorithm for time-series forecasting. The algorithm is especially useful for simple datasets with under 100 time series.
ETS
arn:aws:forecast:::algorithm/ETS
Exponential Smoothing (ETS) is a commonly used statistical algorithm for time-series 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.
Comparing Forecast Algorithms
Use the following table to find the best option for your time series datasets.
| Neural Networks | Flexible Local Algorithms | Baseline Algorithms | ||||
|---|---|---|---|---|---|---|
| CNN-QR | DeepAR+ | Prophet | NPTS | ARIMA | ETS | |
| Computationally intensive training process | High | High | Medium | Low | Low | Low |
| Accepts historical related time series* | ||||||
| Accepts forward-looking related time series* | ||||||
| Accepts item metadata (product color, brand, etc) | ||||||
| Accepts the Weather Index built-in featurization | ||||||
| Suitable for sparse datasets | ||||||
| Performs Hyperparameter Optimization (HPO) | ||||||
| Allows overriding default hyperparameter values | ||||||
*For more information on related time series, see Related Time Series.
