Abstract: Disclosed are a system, method and system to predict an individual's action in connection with a vehicle user interface using machine learning. One or more models may be developed based, at least in part, on observations of past actions by the individual among the plurality of target actions by the individual. Extracted features of a current context may be applied to the developed one or more models to predict a subsequent action among the target actions by the individual.

Abstract: A machine learning system for forecasting demand implements an improved process using event streams for predicting the impact that events in the geographic area surrounding an establishment have on demand forecasts for the establishment. A range of distances from the establishment can be determined and divided up into different distances. Event streams can be generated from the events by grouping the events into combinations of event attributes, such as event category and the distance of the event from the establishment. The event streams can be provided as inputs along with demand data for computing demand forecasts that account for increased or decreased traffic patterns in the geographic area surrounding the event. The event streams can be filtered based on the magnitude of the impact the events have on the demand for the products and/or services of the establishment before, during and after the event takes place.

Abstract: Disclosed is a machine learning system for adapting demand forecasts to varying time slots or intervals within a forecast day, for example when an organization's hours of operation change. The system computes a demand curve for the demand data and thereafter modifies the demand curve using curve shaping operations to adapt the demand curve to new or different time slots. Demand forecasts for the new or different time slots can thereafter be computed using values interpolated from the modified demand curve. In one embodiment, curve shaping operations are performed in a manner in which the peak values for the time slots in the demand forecast are preserved. Peak detection and segmentation operations may also be used in combination with the curve shaping to further adapt the demand forecasts.

Abstract: Disclosed is a machine learning system with date alignment features for improved demand forecasting for products and/or services. The system includes an appliance for more accurately aligning days and weeks between years, including adapting to holidays and special days, in order to ascertain the date in a previous year that most closely aligns with the date in the future for which the forecast is sought. The corresponding day in one or more previous years can then be computed and demand data associated therewith can be retrieved from data storage to be used in forecasting demand on the forecast date. The most closely aligned day from a previous year can be selected such that the aligned day is positioned appropriately within the calendar week and year and the aligned day falls within a week that is positioned appropriately within the calendar month (i.e., first week, last week or middle-month weeks).

Abstract: Disclosed is a system for forecasting demand for goods and/or services. In at least certain embodiments the system is configurable to select a machine learning model from among multiple different machine learning models for forecasting demand for a dataset that may be continually being updated over time. The models available to the system are each based on different machine learning algorithms (e.g., linear regression, gradient boosting, neural network, etc.) as well as several variations for each algorithm available to the system. The system can monitor changes in the datasets, changes in accuracy of the machine learning results, and external factors, and based thereon, determine whether to initiate a model reselection process or a model retraining process. Each machine learning model can be evaluated against each dataset and can select the best model for the dataset.