System and method for determining a transit prediction model

Abstract

A method for prediction model determination can include: determining a set of models, training each model, determining package transit data, evaluating the set of models, selecting a model from the set of models, predicting package transit data using the selected model, and/or any other suitable element.

We claim: 
     
         1 . A system, comprising:
 an interface configured to receive a request for a target package; and   a processing system configured to:
 a) determine actual transit times for a set of packages; 
 b) train a set of models using supervised learning, wherein each model is trained using a different set of historic transit data; 
 c) for each model:
 determine predicted transit times for the set of packages using the model; and 
 determine individual evaluation metrics for each of a set of time periods based on the predicted and actual transit times for the set of packages; 
 
 d) select a model from the set of models based on the individual evaluation metrics for each trained model; and 
 e) predict a transit time for the target package using the selected model; 
   
       wherein the interface returns the predicted transit time for the target package. 
     
     
         2 . The system of  claim 1 , wherein each of the set of packages is associated with an evaluation period, wherein each of the set of time periods is within the evaluation period. 
     
     
         3 . The system of  claim 1 , wherein each of the set of packages is delivered within the evaluation period. 
     
     
         4 . The system of  claim 1 , wherein the processing system is further configured to: for each model, aggregate the individual evaluation metrics to determine an overall evaluation metric, wherein selecting a model from the set of models based on the individual evaluation metrics comprises selecting the model based on the overall evaluation metric for each trained model. 
     
     
         5 . The system of  claim 1 , wherein the interface is further configured to return the predicted transit time for the target package, wherein the predicted transit time for the target package is used to select a shipping carrier service. 
     
     
         6 . The system of  claim 5 , wherein a label is generated for the selected shipping carrier service, wherein the target package is shipped using the label. 
     
     
         7 . A system, comprising:
 a processing system configured to:
 a) determine actual transit times for a set of packages; 
 b) train each of a set of models, wherein each model learns to predict transit times based on historic transit data selected based on a historic training data selection rule associated with the respective model; 
 c) for each trained model:
 determine predicted transit times for the set of packages using the trained model; 
 determine individual evaluation metrics based on the predicted and actual transit times for the set of packages; and 
 aggregate the individual evaluation metrics; and 
 
 d) select a model from the set of models based on the respective aggregated evaluation metrics, wherein the selected model is used to predict a transit time for a target package. 
   
     
     
         8 . The system of  claim 7 , wherein the individual evaluation metrics comprise individual evaluation metrics for each subperiod of an evaluation period. 
     
     
         9 . The system of  claim 8 , wherein the individual evaluation metrics for each subperiod are determined based on the predicted and actual transit times for a subset of packages in the set of packages, wherein each of the subset of packages is delivered within the respective subperiod. 
     
     
         10 . The system of  claim 8 , wherein each subperiod comprises a day. 
     
     
         11 . The system of  claim 7 , wherein the target package is associated with a shipment created within a prediction period, wherein the selected model is used to predict a transit time for each of a set of target packages created within the prediction period. 
     
     
         12 . The system of  claim 11 , wherein the processing system is configured to repeat a)-d) for a successive prediction period, wherein the set of models are retrained for the successive prediction period. 
     
     
         13 . The system of  claim 7 , wherein predicting the transit time for the target package comprises:
 determining target historic transit data based on the historic training data selection rule associated with the selected model; and   retraining the selected model on the target historic transit data, wherein the transit time for the target package is predicted using the retrained selected model.   
     
     
         14 . The system of  claim 7 , wherein each model learns to predict transit times based on a distribution of the selected historic transit data. 
     
     
         15 . The system of  claim 14 , wherein each model learns to predict transit times based on a predetermined percentile of the distribution of the selected historic transit data. 
     
     
         16 . The system of  claim 7 , wherein the historic transit data is associated with at least one of: a shipping carrier, a shipping carrier service, or a shipping lane.