Abstract: A data prediction subsystem stores event data indicating amounts of items removed from locations over a previous period of time and event-to-status transition rules for each location. An event is detected at a first location. The detected event is associated with a change in status of a first item. Based on the detected event and the event-to-status transition rules, an anticipated item status is determined for the first item, indicating whether the first item is believed to be present at the first location at a time during the previous period of time of the event data. Based at least in part on the anticipated item status for the first item, a prediction value is determined that corresponds to a recommended amount of the first item to request for a future time.

Abstract: A data prediction subsystem stores event data indicating amounts of items removed from locations over a previous period of time and event-to-status transition rules for each location. An event is detected at a first location. The detected event is associated with a change in status of a first item. Based on the detected event and the event-to-status transition rules, an anticipated item status is determined for the first item, indicating whether the first item is believed to be present at the first location at a time during the previous period of time of the event data. Based at least in part on the anticipated item status for the first item, a prediction value is determined that corresponds to a recommended amount of the first item to request for a future time.

Abstract: A data prediction subsystem receives event data indicating amounts of items removed from locations over a previous period of time, An event probability is determined based at least in part on a number of concurrent days without detected item removal events for a first item at a first location and an anticipated item removal amount per day. After determining that the event probability is less than the threshold value, an updated status is determined for the first item at the first location. The updated status is an empty status indicating that the first item is not believed to be present at the first location. Based at least in part on the updated status for the first item at the first location, a prediction value is determined corresponding to a recommended amount of the first item to request for a future time.

Abstract: A data prediction subsystem includes receives event data indicating amounts of items removed from locations over a previous period of time. For a first day of the first set of event data having zero events or an empty status indicating that the first item is not believed to be present at the first location, longitudinal and cross-sectional components are determined. An anticipated event value for the first item at the first location is determined using the longitudinal component and the cross-sectional component. Based at least in part on the anticipated event value, a prediction value is determined that corresponds to a recommended amount of the first item to request at a future time.

Abstract: A data prediction subsystem stores hourly event potential data indicating an expected amount of removal events as a function of time of day. Based at least in part on event data, it is determined that a first item associated with a first location has an empty status at a time after a start of a day. For the day, an anticipated event value is determined for the first item at the first location. Using the anticipated event value and the hourly event potential data, a time-adjusted event value is determined. Based at least in part on the time-adjusted event value, a prediction value is determined that corresponds to a recommended amount of the first item to request for a future time.

Abstract: A data prediction subsystem receives event data indicating an amount of an item removed from each of a plurality of locations over a previous period of time. For each location, prediction data is determined using the event data. The prediction data includes, for each day over a future period of time, a non-integer value indicating an anticipated amount of the item that will be removed from the location. An improved rounding process is used to round the prediction value for each day. The resulting prediction data is used to proactively request items with improved communication and computational efficiency.

Abstract: An event tracking subsystem detects events for the removal of items at a plurality of locations. A data prediction subsystem receives event data based on the detected events that indicates an amount of an item removed from each of the plurality of locations over a previous period of time. The data prediction subsystem determines a prediction data value for each item at each of the plurality of locations. The prediction data is used to proactively request items with improved communication and computational efficiency.