Leverage Facebook
Use the power of Facebook's Prophet Statistical Algorithm to predict
Leverage Amazon
Use Amazon's DeepAR Machine Learning models, apply 100s of features to sense demand
Leverage Cloud
Forecast 100s of thousands of SKUs with hierarchical planning parameters
Optimal BalanceTM Forecast
Statistical Forecasting
Facebook Prophet model is a powerful tool
Demand Sensing using ML Models
AWS's DeepAR+ is one of the best
Modern Solution
Optimal BalanceTM support many STAT models and Machine Learning Models
Promotion Management
Create Events and influence future forecast lifts
Forecast Error
Optimal BalanceTM has standard process to calculate forecast error
Optimal BalanceTM Forecast leverages powerful technology to predict
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.
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.
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.
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.
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.
Learn More
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.
Learn More
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.
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.