AWS Supply Chain Demand Planning offers a combination of 25 built-in forecast models to create baseline demand forecasts for products with diverse demand patterns in customers’ datasets. The list of 25 forecast models includes 11 forecast ensemblers (each ensembler is unique based on the set of models that make up the ensembler and/or the metric the ensembler optimizes to) and 14 individual forecast algorithms including statistical algorithms like Autoregressive Integrated and Moving Average (ARIMA) to complex neural network algorithms like CNN-QR, Temporal Fusion Transformer and DeepAR+. Customers have the choice of using forecast ensembler or individual forecast algorithm based on their use case and unique needs. While the forecast ensemblers offer the advantage of customers not having to manually deal with cumbersome tasks such as model selection, hyperparameter tuning and having to simply pick the forecast error metric that is best suited for the customer use case that the ensembler would optimize , the individual forecast algorithms offer flexibility for customer use cases that and best forecasted with a single model instead of an ensemble.
The following table lists the 25 built-in forecast models offered by AWS Supply Chain Demand Planning along with what they are best suited for.
| Type | Forecast Ensembler/Algorithm | Model(s) in Ensemble | Automated hyper Parameter Tuning (Yes/No) | Default Parameters | Metric Optimized | Scenario(s) the model is best suited for | Supports Related Times as Forecast Inputt - Yes/No? |
|---|---|---|---|---|---|---|---|
|
Forecast Model(s) Ensembler |
AutoGluon Best Quality (MAPE) |
Ensemble of baseline, statistical , ML/Deep learning models in the AutoGluon |
Yes |
AutoGluon best_quality preset |
MAPE (Mean Absolute Percentage Error) |
Automated Ensemble without need for manual model assignment/selection. |
Yes, Past and Future Related Time Series |
|
Forecast Model(s) Ensembler |
AutoGluon Best Quality (WAPE) |
Ensemble of baseline, statistical , ML/Deep learning models in the AutoGluon |
Yes |
AutoGluon best_quality preset |
WAPE (Weighted Absolute Percentage Error) |
Automated Ensemble without need for manual model assignment/selection. |
Yes, Past and Future Related Time Series |
|
Forecast Model(s) Ensembler |
AutoGluon Best Quality (MASE) |
Ensemble of baseline, statistical , ML/Deep learning models in the AutoGluon |
Yes |
AutoGluon best_quality preset |
MASE (Mean Absolute Scaled Error) |
Automated Ensemble without need for manual model assignment/selection. |
Yes, Past and Future Related Time Series |
|
Forecast Model(s) Ensembler |
AutoGluon Best Quality (RMSE) |
Ensemble of baseline, statistical , ML/Deep learning models in the AutoGluon |
Yes |
AutoGluon best_quality preset |
RMSE (Root Mean Squared Error) |
Automated Ensemble without need for manual model assignment/selection. |
Yes, Past and Future Related Time Series |
|
Forecast Model(s) Ensembler |
AutoGluon Best Quality (WCD) |
Ensemble of baseline, statistical , ML/Deep learning models in the AutoGluon |
Yes |
AutoGluon best_quality preset |
WCD (Weighted Cumulative Deviation) |
Automated Ensemble without need for manual model assignment/selection. |
Yes, Past and Future Related Time Series |
|
Forecast Model(s) Ensembler |
AutoGluon StatEnsemble (MAPE) |
Ensemble of all statistical models(only) in the AutoGluon |
Yes |
AutoGluon all Supported Stats Model |
MAPE (Mean Absolute Percentage Error) |
Automated Ensemble without need for manual model assignment/selection. |
No |
|
Forecast Model(s) Ensembler |
AutoGluon StatEnsemble (WAPE) |
Ensemble of all statistical models(only) in the AutoGluon |
Yes |
AutoGluon all Supported Stats Model |
WAPE (Weighted Absolute Percentage Error) |
Automated Ensemble without need for manual model assignment/selection. |
No |
|
Forecast Model(s) Ensembler |
AutoGluon StatEnsemble (MASE) |
Ensemble of all statistical models(only) in the AutoGluon |
Yes |
AutoGluon all Supported Stats Model |
MASE (Mean Absolute Scaled Error) |
Automated Ensemble without need for manual model assignment/selection. |
No |
|
Forecast Model(s) Ensembler |
AutoGluon StatEnsemble (RMSE) |
Ensemble of all statistical models(only) in the AutoGluon |
Yes |
AutoGluon all Supported Stats Model |
RMSE (Root Mean Squared Error) |
Automated Ensemble without need for manual model assignment/selection. |
No |
|
Forecast Model(s) Ensembler |
AutoGluon StatEnsemble (WCD) |
Ensemble of all statistical models(only) in the AutoGluon |
Yes |
AutoGluon all Supported Stats Model |
WCD (Weighted Cumulative Deviation |
Automated Ensemble without need for manual model assignment/selection. |
No |
|
Forecast Model(s) Ensembler |
AWS Supply Chain AutoML |
Ensemble of all in Amazon Forecast AutoML. |
Not Applicable |
AutoML default settings |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Automated Ensemble without need for manual model assignment/selection. |
Depends on Selected Models by Ensembler. |
|
Forecast Algorithm |
CNN-QR |
CNN-QR (Convolutional Neural Network - Quantile Regression) is a machine learning algorithm for time series forecasting using causal convolutional neural networks (CNNs). |
Not Applicable |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Best suited for large datasets containing hundreds of time series. |
Yes, Past and Future Related Time Series |
|
|
Forecast Algorithm |
DeepAR+ |
DeepAR+ is a machine learning algorithm for time series forecasting using recurrent neural networks (RNNs). |
Not Applicable |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Best suited for large datasets containing hundreds of time series. |
Yes, Past and Future Related Time Series |
|
|
Forecast Algorithm |
LightGBM |
Light Gradient-Boosting Machine (LGBM) is a tabular machine learning model that uses historical demand data from past seasons. |
Not Applicable |
LightGBM default parameters |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Best suited for datasets where different items share similar demand trends. Less effective on datasets with diverse item characteristics and demand patterns. |
No |
|
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. |
Not Applicable |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Best suited for time series that have strong seasonal effects and several seasons of historical data. |
Yes, Past and Future Related Time Series |
|
|
Forecast Algorithm |
Triple Exponential Smoothing |
Exponential Smoothing (ETS) is a statistical model for time series forecasting. |
Not Applicable |
Default ETS parameters |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Best suited for datasets with seasonality patterns, computing weighted averages of past observations with exponentially decreasing weights. ETS is most effective for time series with fewer than 100 items. |
No |
|
Forecast Algorithm |
Auto Complex Exponential Smoothing (AutoCES) |
Auto Complex Exponential Smoothing is an advanced variant of exponential smoothing, automatically adjusts smoothing parameters, offering accurate forecasts for time series with intricate seasonal structures. |
Not Applicable |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Best suited for complex seasonal patterns in time series data, including multiple seasonality or irregular cycles. |
No |
|
|
Forecast Algorithm |
ARIMA |
ARIMA (Auto-Regressive Integrated Moving Average) is a statistical model for time series forecasting. It combines autoregressive, moving average, and differencing components to model trends. |
Not Applicable |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Best suited for datasets without strong seasonal effects. |
No |
|
|
Forecast Algorithm |
Seasonal ARIMA |
SARIMA (Seasonal Auto-Regressive Integrated Moving Average) is an extension of ARIMA that includes seasonal components, It models both non-seasonal and seasonal trends, ensuring accurate predictions for datasets with multiple seasons of historical data. |
Not Applicable |
Seasonal ARIMA default parameters |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Best suited for time series with strong seasonal patterns. |
No |
|
Forecast Algorithm |
Theta |
The Theta model is a time series forecasting method that combines exponential smoothing with a decomposition approach to handle trend, seasonality, and noise. It uses a linear trend model and non-linear smoothing components to capture both short-term and long-term patterns, often outperforming traditional methods. |
Not Applicable |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Best suited for intermittent demand forecasting. |
No |
|
|
Forecast Algorithm |
Aggregate-Disaggregate Intermittent Demand Approach (ADIDA) |
ADIDAaggregates data at a higher level to capture broader patterns, then disaggregates it for accurate forecasts improves accuracy by reducing noise. |
Not Applicable |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Best suited for products with low or irregular demand, intermittent demand. |
No |
|
|
Forecast Algorithm |
Croston |
The Croston method is designed for intermittent demand forecasting. It separates demand into two components the size of non-zero demands and the intervals between them. These components are independently forecasted and combined. |
Not Applicable |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Best suited for intermittent demand forecasting. |
No |
|
|
Forecast Algorithm |
Intermittent Multiple Aggregation Prediction Algorithm (IMAPA) |
IMAPA is a forecasting method for intermittent demand data, where demand is irregular with many zero values. It aggregates data at multiple levels to capture different demand patterns, offering more robust predictions for datasets with highly irregular demand compared to methods like Croston. |
Not Applicable |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Best suited for improving accuracy for intermittent demand patterns (compared to traditional methods like exponential smoothing). |
No |
|
|
Forecast Algorithm |
Moving Average |
The Moving Average model forecasts by averaging past data points over a fixed window. |
Not Applicable |
Moving Average default parameters |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Best suited for short-term forecasts, especially in sparse data scenarios. This method performs well on time series with simple trends, providing quick, easy predictions without requiring complex modeling. |
No |
|
Forecast Algorithm |
Non Parametric Time Series (NPTS) |
NPTS is a baseline forecasting method for sparse or intermittent time series data. It includes variants such as Standard NPTS and Seasonal NPTS. |
Not Applicable |
WQL (Weighted Quantile Loss) for P10, P50, P90 |
Best suited for robust predictions for irregular time series by handling missing data and seasonal effects. It is scalable and effective for irregular demand data. |
No |
The following table lists the metrics available in Support Demand Planning forecast models.
| Metric | Metric Description | Metric Formula | When to use this metric to optimize | Link |
|---|---|---|---|---|
|
MAPE |
MAPE measures the average magnitude of the errors in a set of forecasts, expressed as a percentage of the actual values. |
Not Applicable |
It is commonly used for evaluating the accuracy of predictive models, especially in time series forecasting, where all time series are treated equal for forecast error evaluation. |
|
|
WAPE |
WAPE is a variation of MAPE that considers the weighted contributions of different data points. |
Not Applicable |
It is particularly useful when the data has varying importance or when some observations are more significant than others. |
|
|
RMSE |
RMSE measures the square root of the average squared differences between predicted and actual values. |
Not Applicable |
RMSE is sensitive to large errors because of the squaring operation, which gives more weight to larger errors.In use cases where only a few large mispredictions can be very costly, the RMSE is the more relevant metric. |
|
|
WCD |
WCD is a measure of cumulative forecast error weighted by a set of predetermined weights. |
Not Applicable |
This metric is often used in applications where certain time periods, products, or data points have more importance than others, allowing for prioritization in the error analysis. |
Not Applicable |
|
wQL |
wQL is a loss function that evaluates the performance of a model based on quantiles, with weighted contributions from different data points. |
Not Applicable |
It’s useful for assessing model performance in scenarios where the importance of different quantiles (e.g., 90th percentile, 50th percentile) or observations varies. It is particularly useful when there are different costs for underpredicting and overpredicting. |
|
|
MASE |
MASE (Mean Absolute Scaled Error) is a performance metric used to evaluate the accuracy of time series forecasting models. It compares the mean absolute error (MAE) of the forecasted values to the mean absolute error of a naive forecast. |
Not Applicable |
MASE is ideal for datasets that are cyclical in nature or have seasonal properties. For example, forecasting for items that are in high demand during summers and in low demand during winters can benefit from taking into account the seasonal impact. |
