*Optimal Stat&ML Forecast is a forecasting solution*## Statistical Forecast

Optimal Forecast comes with multiple statistical models to support trend, seasonality and promotions

## Machine Learning Forecast

Runs on the powerful DeepAR ML Model. You can use multiple of time series data to predict your demand

## Forecast Error

Full automated solution to perform forecast error and do automatic Model Evaluation for better algorithms

## Optimal Balance^{TM} Forecast

###
**Leverage Statistical Algorithms**

**Optimal Balance supports Prophet, NPTS, ARIMA, and ETS**

###
**Leverage Machine Learning Models**

**Models include Convolutional Neural Network and DeepAR+**

## Leverage Public Cloud

Optimal Balance^{TM} is fully integrated with public cloud to run large forecasting models.

## Promotion Management

Manage your promotions, predict based on past performance.

## Automatic Model Selection

Optimal BalanceTM has automatic model selection process based on forecast accuracy.

## Optimal Balance^{TM} Forecast leverages powerful technology to predict

**Prophet**

**NPTS**

**ARIMA**

**ETS**

**CNN-QR**

**DeepAR+**

**Prophet**

Developed by Facebook

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**

Non-Parametric Time Series

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**

Autoregressive Integrated Moving Average.

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**

Exponential Smoothing

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.

**CNN-QR**

Convolutional Neural Network.

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+**

Developed by Amazon

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.

Learn More

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.

Learn More

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.

Learn More

Exponential Smoothing

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.

Convolutional Neural Network.

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.

Developed by Amazon

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.