Abstract: Embodiments forecast demand of an item by receiving historical sales data for the item for a plurality of past time periods including a plurality of features that define one or more feature sets. Embodiments use the feature sets as inputs to one or more different algorithms to generate a plurality of different models. Embodiments train each of the different models. Embodiments use each of the trained models to generate a plurality of past demand forecasts for each of some or all of the past time periods. Embodiments determine a root-mean-square error (“RMSE”) for each of the past demand forecasts and, based on the RMSE, determine a weight for each of the trained models and normalize each weight. Embodiments then generate a final demand forecast for the item for each future time period by combining a weighted value for each trained model.

Abstract: Embodiments generate a model of demand of a product that includes an optimized feature set. Embodiments receive sales history for the product and receive a set of relevant features for the product and designate a subset of the relevant features as mandatory features. From the sales history, embodiments form a training dataset and a validation dataset and randomly select from the set of relevant features one or more optional features. Embodiments include the selected optional features with the mandatory features to create a feature test set. Embodiments train an algorithm using the training dataset and the feature test set to generate a trained algorithm and calculate an early stopping metric using the trained algorithm and the validation dataset. When the early stopping metric is below a predefined threshold, the feature test set is the optimized feature set.

Abstract: Embodiments predict future demand for a first product by receiving historical sales data for an aggregate products/locations level, the historical sales data including a plurality of sales data points, including sales data points for the first product at each of a plurality of locations. Embodiments extract a plurality of different types of features related to sales of each of the products and generate a plurality of clusters of sales data points based on the plurality of different types of features. Embodiments train each of the clusters to generate a plurality of trained cluster models including promotion effects per cluster. For a particular time period, a particular location and the first product, embodiment identify the features for the time period and map to one of the trained cluster models to fetch the promotion effects for the time period. Embodiments then use the promotion effects to forecast demand for the first product.

Abstract: Embodiments generate a short life cycle sales curve for a short life cycle item. Embodiments generate a plurality of similar sales curves corresponding to at least one similar item that is similar to the short life cycle item. Embodiments parametrize each of the similar sales curves, including estimating, for each similar sales curve, a coefficient of innovation parameter, a coefficient of imitation parameter, and an error parameter, and determine a weight for each error parameter. Embodiments combine the coefficient of innovation parameters and the coefficient of imitation parameters using the weights, and generate the short life cycle sales curve using the combined coefficient of innovation parameters and the combined coefficient of imitation parameters.

Abstract: Embodiments generate a short life cycle sales curve for a short life cycle item. Embodiments generate a plurality of similar sales curves corresponding to at least one similar item that is similar to the short life cycle item. Embodiments parametrize each of the similar sales curves, including estimating, for each similar sales curve, a coefficient of innovation parameter, a coefficient of imitation parameter, and an error parameter, and determine a weight for each error parameter. Embodiments combine the coefficient of innovation parameters and the coefficient of imitation parameters using the weights, and generate the short life cycle sales curve using the combined coefficient of innovation parameters and the combined coefficient of imitation parameters.

Abstract: Embodiments predict future demand for a first product by receiving historical sales data for an aggregate products/locations level, the historical sales data including a plurality of sales data points, including sales data points for the first product at each of a plurality of locations. Embodiments extract a plurality of different types of features related to sales of each of the products and generate a plurality of clusters of sales data points based on the plurality of different types of features. Embodiments train each of the clusters to generate a plurality of trained cluster models including promotion effects per cluster. For a particular time period, a particular location and the first product, embodiment identify the features for the time period and map to one of the trained cluster models to fetch the promotion effects for the time period. Embodiments then use the promotion effects to forecast demand for the first product.

Abstract: Embodiments select demand forecast parameters for a demand model for one or more items, receive historical sales data for the items on a per store basis and receive a plurality of seasonality curves for a first item. Embodiments determine a repeatability of each of the seasonality curves using a correlation of each seasonality curve over year to year demand and retain a first seasonality curve based on the repeatability. Embodiments determine a smoothness of the first seasonality curve and determine a sparsity of the first seasonality curve. Based on the determined repeatability, smoothness and sparsity, embodiments determine that the first seasonality curve is reliable and repeat the receiving the plurality of seasonality curves, determining the repeatability, determining the smoothness, and determining the sparsity to determine a plurality of reliable seasonality curves. Embodiments use the demand model and the reliable seasonality curves and determine a demand forecast for the first item.

Abstract: Embodiments predict/forecast demand of a product by receiving historical sales data for the product and, using a plurality of different seasonality estimation methods, estimating a plurality of different seasonality estimations for future time periods and determining an approximate error amount for each of the different seasonality estimations. Embodiments determine a weight for each of the plurality of different seasonality estimation methods based on the corresponding approximate error amount and generate an aggregate seasonality model based on the plurality of different seasonality estimations and the weights. Embodiments then determine a demand forecast using the aggregate seasonality model.

Abstract: Systems, methods, and other embodiments are disclosed that are configured to characterize an effect on sales of a retail item due to a sales promotion. In one embodiment, first sales data for the retail item is retrieved from a plurality of stores that have applied the sales promotion for the retail item. Second sales data for the retail item is retrieved from a single store that has applied the sales promotion for the retail item. A combined promotion effect value is generated based on the first sales data and the second sales data. The combined promotion effect value characterizes an effect on sales of the retail item as sold by the single store due to the sales promotion.

Abstract: Embodiments forecasting future demand for an item. Embodiments receive a regression based demand algorithm for the item that includes the set of features as regression variables and split the data points into a training set and a testing set. Embodiments assign each of the features of the set of features into one of a plurality of regularization categories and assign a penalty parameter to each of the features subject to regularization. Embodiments train the demand algorithm using the training set, the penalty parameters and the features to generate a trained demand model. Embodiments evaluate the trained demand model using the testing set to determine an early drop metric and repeat the assigning each of the features, the assigning the penalty parameter, the training the demand algorithm and the evaluating the trained demand model until the early drop metric meets a threshold.

Abstract: Systems and methods are provided for performing customizable machine prediction using an extensible software tool. A specification including features of a trained machine learning model can be received and an interface for the trained machine learning model can be generated. The trained machine learning model can be loaded using the interface, the loaded machine learning model including a binary file configured to receive data as input and generate prediction data as output. Predictions can be generated using observed data that is stored according to a multidimensional data model, wherein a portion of the observed data is input to the loaded machine learning model to generate first data predictions, and a portion of the observed data is used by a generic forecast model to generate second data predictions. The first and second data predictions can be displayed in a user interface configured to display intersections of the multidimensional data model.

Abstract: Embodiments forecasting future demand for an item. Embodiments receive a regression based demand algorithm for the item that includes the set of features as regression variables and split the data points into a training set and a testing set. Embodiments assign each of the features of the set of features into one of a plurality of regularization categories and assign a penalty parameter to each of the features subject to regularization. Embodiments train the demand algorithm using the training set, the penalty parameters and the features to generate a trained demand model. Embodiments evaluate the trained demand model using the testing set to determine an early drop metric and repeat the assigning each of the features, the assigning the penalty parameter, the training the demand algorithm and the evaluating the trained demand model until the early drop metric meets a threshold.

Abstract: Embodiments select demand forecast parameters for a demand model for a first item. Embodiments receive historical sales data for a plurality of items on a per store basis and receive a plurality of seasonality curves for the first item of the plurality of items, each seasonality curve corresponding to a different pooling level for the first item. Embodiments determine a correlation for each of the seasonality curves at each pooling level and determine a root mean squared error (“RMSE”) for each determined correlation. Embodiments determine a score for each pooling level, the score based on the corresponding correlation, RMSE and a penalty and select one of the seasonality curves based on the determined scores. Embodiments use the demand model and the selected seasonality curve to determine a demand forecast for the first item, the demand forecast including a prediction of future sales data for the first item.

Abstract: Embodiments select demand forecast parameters for a demand model for one or more items, receive historical sales data for the items on a per store basis and receive a plurality of seasonality curves for a first item. Embodiments determine a repeatability of each of the seasonality curves using a correlation of each seasonality curve over year to year demand and retain a first seasonality curve based on the repeatability. Embodiments determine a smoothness of the first seasonality curve and determine a sparsity of the first seasonality curve. Based on the determined repeatability, smoothness and sparsity, embodiments determine that the first seasonality curve is reliable and repeat the receiving the plurality of seasonality curves, determining the repeatability, determining the smoothness, and determining the sparsity to determine a plurality of reliable seasonality curves. Embodiments use the demand model and the reliable seasonality curves and determine a demand forecast for the first item.

Abstract: Embodiments generate a model of demand of a product that includes an optimized feature set. Embodiments receive sales history for the product and receive a set of relevant features for the product and designate a subset of the relevant features as mandatory features. From the sales history, embodiments form a training dataset and a validation dataset and randomly select from the set of relevant features one or more optional features. Embodiments include the selected optional features with the mandatory features to create a feature test set. Embodiments train an algorithm using the training dataset and the feature test set to generate a trained algorithm and calculate an early stopping metric using the trained algorithm and the validation dataset. When the early stopping metric is below a predefined threshold, the feature test set is the optimized feature set.

Abstract: Embodiments forecast demand of an item by receiving historical sales data for the item for a plurality of past time periods including a plurality of features that define one or more feature sets. Embodiments use the feature sets as inputs to one or more different algorithms to generate a plurality of different models. Embodiments train each of the different models. Embodiments use each of the trained models to generate a plurality of past demand forecasts for each of some or all of the past time periods. Embodiments determine a root-mean-square error (“RMSE”) for each of the past demand forecasts and, based on the RMSE, determine a weight for each of the trained models and normalize each weight. Embodiments then generate a final demand forecast for the item for each future time period by combining a weighted value for each trained model.

Abstract: A system for forecasting sales of a retail item receives historical sales data of a class of a retail item, the historical sales data including past sales and promotions of the retail item across a plurality of past time periods. The system aggregates the historical sales to form a training dataset having a plurality of data points. The system randomly samples the training dataset to form a plurality of different training sets and a plurality of validation sets that correspond to the training sets, where each combination of a training set and a validation set forms all of the plurality of data points. The system trains multiple models using each training set, and using each corresponding validation set to validate each trained model and calculate an error. The system then calculates model weights for each model, outputs a model combination including for each model a forecast and a weight, and generates a forecast of future sales based on the model combination.

Abstract: Systems, methods, and other embodiments are disclosed that are configured to generate replenishment parameters for use by an external replenishment system. In one embodiment, sales statistics are generated for an item based at least in part on historical sales data for the item. A determination is made as to if demand forecast data is available for the item. If demand forecast data is not available, an order-point value for the item is generated based at least in part on the sales statistics. If demand forecast data is available, demand forecast statistics are generated and the order-point value is generated based at least in part on the sales statistics and the demand forecast statistics. An order-up-to-level is generated based at least in part on the order-point value.

Abstract: A map customization module builds a customized map for a user based on the user's interest and historical activities. A database stores processed map data including layer data, element data and/or tile data related to maps. The map customization module obtains the necessary processed map data from the database and combines the map data with the user's interests to generate a customized map. The map customization module recognizes the user's interests based on explicit user input and/or implicit user input.

Abstract: Systems, methods, and other embodiments are disclosed that are configured to characterize an effect on sales of a retail item due to a sales promotion. In one embodiment, first sales data for the retail item is retrieved from a plurality of stores that have applied the sales promotion for the retail item. Second sales data for the retail item is retrieved from a single store that has applied the sales promotion for the retail item. A combined promotion effect value is generated based on the first sales data and the second sales data. The combined promotion effect value characterizes an effect on sales of the retail item as sold by the single store due to the sales promotion.