Patent Publication Number: US-11379865-B2

Title: Machine learned models for item price planning

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 63/053,406, filed Jul. 17, 2020, entitled “MACHINE LEARNED MODELS FOR CROP PLANNING,” which is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     This document generally relates to machine learning. More specifically, this document relates to machine learned models for item price planning. 
     BACKGROUND 
     The production and distribution of certain types of items are very important to the operation of a country. For example, farming plays a vital role in the economy, and costs and overhead related to growing crops can be a significant drag on the farming sector specifically. The success of farmers drives the agricultural industry and provides food security and nourishment to individuals across the globe. 
     Success in farming is typically measured in terms of production (yield per acre) and the price received from the crop sale. A good crop rotation plan is critical in maximizing yield per acre. A typical crop rotation plan identifies crops to plant several years out into the future, but is carried out without any visibility into future market prices for those crops. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The present disclosure is illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements. 
         FIG. 1  is a block diagram illustrating a system in accordance with an example embodiment. 
         FIG. 2  is a block diagram illustrating a program logic in accordance with an example embodiment. 
         FIG. 3  is a flow diagram illustrating a method for training and using machine learned models in accordance with an example embodiment. 
         FIG. 4  is a screen capture illustrating an example user input screen in accordance with an example embodiment. 
         FIG. 5  is a screen capture illustrating another example user input screen in accordance with another example embodiment. 
         FIG. 6  is a screen capture illustrating another example user input screen in accordance with another example embodiment. 
         FIG. 7  is a screen capture illustrating another example user input screen in accordance with another example embodiment. 
         FIG. 8  is a screen capture illustrating an example output screen in accordance with an example embodiment. 
         FIG. 9  is a block diagram illustrating an architecture of software, which can be installed on any one or more of the devices described herein. 
         FIG. 10  illustrates a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The description that follows discusses illustrative systems, methods, techniques, instruction sequences, and computing machine program products. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide an understanding of various example embodiments of the present subject matter. It will be evident, however, to those skilled in the art, that various example embodiments of the present subject matter may be practiced without these specific details. 
     The present disclosure describes the use of machine learning to aid in the prediction of item costs and item-related costs. The present disclosure will describe the functionality primarily using the example of crop planning and farming. Nothing in this disclosure, however, shall be read as limiting the scope of coverage to only the crop planning and farming environments, as generally the same techniques can be applied to many different types of items. Examples of items other than crop commodities would include items transacted in bulk, such as the production of crude oil or a gasoline terminal. 
     With respect to farming, typically a farmer reviews the crop rotation plan prior to each planting season, in terms of the weather forecast, input cost, soil situation, and current market price. This review may result in a re-evaluation of the crop rotation plan. 
     When the crop is ready to be harvested, the farmer then typically reviews the current market situation again to decide a sale date for the harvest, in order to maximize farm income. The decision could be to, for example, delay crop sale and keep it in storage until price can be maximized. 
     Many crops, such as grain and oilseeds, have quite volatile prices. The commodity price may change on a daily basis. With farmers needing to make decisions ranging from long term planning (crop rotation), planning at the beginning of each seeding season, deciding whether to sell in advance of harvest by locking in price, selling at harvest, or storing (some or all of) the commodity, there is a need to help farmers make these decisions based on forecasted crop procurement price at nearby grain elevator(s). 
     While machine learning has been applied to the forecasting of commodity prices, prior art models are designed for commodity traders, and not for farmers, and thus fail to capture nuances required to make accurate predictions for crop planning. Elevators are businesses that purchase crops from farmers, and can essentially be thought of as wholesale distributors of the crops. In the non-farming embodiments, the equivalent would be a distribution point, such as a fuel terminal. The general commodity price predicted by prior art models may be forecast at the national level, and may not account for location-based or other personalized nuances specific to individual elevators in proximity to a farmer&#39;s land. Basis price history, however, may not be available for many elevators. 
     In an example embodiment, multiple machine learned models are used to continually learn from data to update various prediction models. Prediction of agricultural commodity prices, at the crop and farm level, and utilizing this information along with crop yield, operational cost, and storage cost, may be used to solve the long- and short-term planning problems of farmers and help in decision making based on daily and the most up-to-date forecasts. 
       FIG. 1  is a block diagram illustrating a system  100  in accordance with an example embodiment. The system  100  may include an in-memory database  102 . An in-memory database (also known as an in-memory database management system) is a type of database management system that primarily relies on main memory for computer data storage. It is contrasted with database management systems that employ a disk storage mechanism. In-memory databases are traditionally faster than disk storage databases because disk access is slower than memory access. One example of an in-memory database is the HANA® database from SAP SE, of Walldorf, Germany. The in-memory database  102  may store data in one or more application tables. In an example embodiment, data feed application program interfaces (APIs)  104 A,  104 B connect the in-memory database  102  to external databases with historical and current pricing information. For example, API  104 A may connect the in-memory database  102  to a price feed regarding local elevator basis prices for each crop procured and stored at the elevator, while API  104 B may connect the in-memory database  102  to a national commodity prices (such as publicly traded derivatives, such as futures contract, with respect to various grains and oilseeds). This information may then be stored in the in-memory database  102 . It should be noted that in one example embodiment, the in-memory database  102  is operated in the cloud. 
     A data intelligence application  106  may include program logic  108 , which may implement rules obtained from a rules framework  110  and machine learned models from a machine learning library  112  to provide price predictions of commodity based on the information stored in the in-memory database  102 . As will be described in more detail below, the program logic  108  may train some of the machine learned models in the machine learning library  112  as well as utilize those models, in addition to utilizing models trained by other components, possibly even on external sources. 
     Also depicted is a front-end service  114 , which utilizes the commodity price predictions in one or more practical ways depending upon the software environment. For example, the front-end service  114  may be a tool that allows a user, such as a farmer, to enter a crop rotation plan and be provided with a list of projected prices (and earnings) for a number of different crops and/or elevators. Alternatively, the front-end service  114  may be a tool that allows the user to enter (either directly or indirectly), a crop and a desired sell date, and then provides this information to the program logic  108 , which returns predicted prices for elevators in proximity to the user&#39;s farm on the desired sell date. To that end, one or more global map and route service APIs  116  may interact with the program logic  108  to aid in determining nearby grain elevators. 
       FIG. 2  is a block diagram illustrating the program logic  108  in accordance with an example embodiment. Here, the logic  108  includes, for agricultural commodity, two separate models for forecasting the commodity prices at a national level. A nearby settlement date machine learned model  200 A builds continuous historical data for each pricing date by reading the pricing from the first nearby settlement date. A settlement date is the date the future contract on a trading exchange is settled. Since the trading happens only on workdays, any gaps that exist in pricing data due to weekends or holidays may be filled by taking the next working day price if the day in question is a Sunday and the prior working day price if it is anything other than a Sunday (e.g., if it is a Saturday, or a holiday that falls on a Wednesday). A constant settlement date machine learned model  202 A, however, keeps the settlement date constant (as per first nearby date for each forecast date), although pricing gaps are still filled based on the rules outlined above with respect to the nearby settlement date machine learned model  200 A. The result is that the constant settlement date machine learned model  202 A provides more continuity by keeping the settlement date constant, and thus provides a smooth baseline for projecting forecasts, but the underlying price information may not be as strong as the nearby settlement date machine learned model  200 A. 
     Since there are certain circumstances in which the nearby settlement date machine learned model  200 A will perform better and other circumstances where the constant settlement date machine learned model  202 A will perform better, in an example embodiment a settlement date machine learned model selection model  204 A will select the results of one of these two models based on the current circumstances. Root mean square error is determined for each of the models to define the best model based on data trend and seasonality. It should be noted that while root mean square error is one factor, it need not be the only factor, and in an example embodiment, the settlement date machine learned model selection model  204 A is itself a trained model that may use factors such as mean percentage error and the historical trend of a winning model between the nearby settlement date machine learned model  200 A and the constant settlement date machine learned model  202 A. In case there is a difference in this trend while the root mean square error difference is small (e.g., within a preset amount), the trend may be followed. The root mean square error scores for each model  200 A,  202 A may be stored in the application tables of the in-memory database to keep a record and establish the trend. 
     The output of the settlement date machine learned model selection model  204 A is a forecast time series (from the appropriate model) of the national commodity prices. These forecasts may project into the future a certain amount, e.g., up to 180 days. 
     The basis price forecast should also be determined. Local purchase price at the commodity elevator is different than the ongoing derivative national market price and is influenced by basis. Local crop price minus the derivative market price is the basis. Basis can be either positive or negative. 
     In an example embodiment, a mechanism is built to store the prevailing basis price in the application tables along with elevator addresses to build a basis price history nationally. A process that will be termed Robotic Basis Price Automation can be used to extract and store basis prices for elevators from online publications, or corresponding APIs may be used where available. Robotic Basis Price Automation process may leverage SAP artificial intelligence business services e.g. SAP Document Information Extraction to extract basis price information from online publications or elevator websites coupled with another machine learning service e.g. SAP Intelligent Robotic Process Automation to automate this process and execute in a periodic manner. 
     A first basis price prediction machine learned model  206 A for each commodity may be used to forecast the basis price of the corresponding commodity based on available basis price history for each elevator. The first basis price prediction machine learned model  206 A is trained using historical data to create a model based on data trend and seasonality. 
     A second basis price prediction machine learned model  208 A for each commodity may be used to forecast the basis price of the corresponding commodity at a specific elevator based on a weighted average of all “nearby” elevators. The weighted average is based on the proximity of the nearby elevators, with the nearest elevator carrying the highest weight. The nearby range is a function of parameters, such as geographic distance, that allow for a minimum number of elevators or elevators based on a maximum radius to drive. This may be important for elevators for which basis price history is not available. It additionally serves as an alternative way to forecast basis on elevators that have historical prices available but during certain periods of time the basis price has been an outlier. An example of such a scenario is the following: to meet an export obligation at a New Orleans elevator to export a particular commodity at a particular volume, the price at an interior elevator, which supplies the commodity to the New Orleans elevator, is made favorable as an outlier until the desired volume has been received. After that, the price returns to normal. The incorporation of nearby elevators in the second basis price prediction machine learned model  208 A helps protect from such scenarios ruining the accuracy of the model. 
     Since there are certain circumstances in which the first basis price prediction machine learned model  206 A will perform better and other circumstances where the second basis price prediction machine learned model  208 A will perform better, in an example embodiment a basis price machine learned model selection model  210 A will select the results of one of these two models based on the current circumstances. Root mean square error is determined for each of the models to define the best model based on data trend and seasonality. It should be noted that while root mean square error is one factor, it need not be the only factor, and in an example embodiment, the basis price machine learned model selection model  210 A is itself a trained machine learned model that may use factors such as mean percentage error and the historical trend of a winning model between the first basis price prediction machine learned model  206 A and the second basis price prediction machine learned model  208 A. In case there is a difference in this trend while the root mean square error difference is small (e.g., within a preset amount), the trend may be followed. The root mean square error scores for each model  206 A,  208 A, may be stored in the application tables of the in-memory database to keep a record and establish the trend. 
     A forecast blending component  212  may then blend the forecasts from the settlement date machine learned model selection model  204 A the basis price machine learned model selection model  210 A along with the storage costs as per each forecast date for each commodity. For a flat storage rate, the total storage cost for each date range in the time series is calculated using the following formula: Storage Cost=(Forecast Date−Harvest Date)*Total Yield*Storage Rate. The storage rate can be stipulated in different ways, e.g. tiered graduated scale storage rate based on quantity and accordingly the calculation formula will be different. Storage information may be used for sale date determination but may not, in some example embodiments, be used for crop rotation planning and crop pre-planting planning. 
     For each date in the time series, the forecast blending component  212  combines the forecast commodity price, the forecast basis price, the input cost, and the storage cost as follows:
 
Crop Earning=Forecast Commodity Price+Forecast Basis Price−Input Cost−Storage Cost.
 
The input cost and storage rate may be received from user entry. The result is a crop earning forecast for each date. Input cost represents total dollar value of all the inputs to grow a specific crop. This may include seed, fertilizer, labor, machinery, freight cost to transport the harvest to elevator etc. Storage rate represents how much is charged for grain storage. Storage rate may be flat, volume based on tiered graduated scale etc.
 
     In some example embodiments, if crop rotation planning is performed, a crop planning component  214  then obtains a crop earning forecast from the forecast blending component  212  for each of a plurality of different crops, thus obtaining, for example, a price forecast for many different crops the farmer is considering planting. 
     A display component  216  then carries out the final decision making as to how to present results to the user. For crop rotation planning, for example, the crop earning forecasts from the crop planning component  214  and a sale date are obtained, and an expected crop income is presented to the user. As per a rules framework, a date range may also be presented around the harvest date specified by the farmer to indicate movement in forecasted income. 
     For “crop pre-planting planning” model, for example, display component  216  receives crop earning forecast from forecast blending component  212 . As per a rules framework, a date range may also be presented around the requested harvest/sale date to indicate movement in forecasted income. Farmer can use this information to decide if it is advantageous to sell the crop prior to harvest, to lock in a better price, or to wait until the crop is ready. 
     For “best sale date planning at harvest” an additional parameter is storage rate. The crop earning forecast received from forecast blending component  212  is reviewed by display component  216  to identify specific date(s) when the crop income is highest. The output of this model along with rules framework is to present the date range in which the best crop income is expected. This will help the farmer decide how long the crop should be kept in storage. 
     For “best sale date planning post-harvest”, display component  216  receives earning forecast timeseries from forecast blending component  212 . It identifies date range along with rules framework when the crop income is highest and presents it to the user. In this scenario input cost from the farmer should be inclusive of storage rate incurred thus far to allow this invention to present the true crop income. 
       FIG. 3  is a flow diagram illustrating a method  300  for training and using machine learned models in accordance with an example embodiment. At operation  302 , an identification of a crop commodity and an identification of a crop elevator corresponding to the crop commodity are received from a graphical user interface or global map and route service API. At operation  304 , price history of basis prices paid for the crop commodity by the crop elevator on a plurality of past dates are obtained. At operation  306 , the price history is fed into a first basis prediction machine learned model trained to forecast a basis price for a plurality of future dates based upon the price history. At operation  308 , a location of a crop elevator is obtained. At operation  310 , one or more crop elevators nearby the crop elevator are identified, based on criteria maintained by a rules framework. This criterion may include, for example, strict geographic distance (“as the crow flies”), road travel distance (between two locations using roads), or travel time. There may be a secondary criterion on minimum number of elevators to be selected, which may result in increase in proximity definition until a minimum number of elevators are identified. 
     At operation  312 , price history of basis prices paid for the crop commodity by the one or more crop elevators nearby the crop elevator is obtained. At operation  314 , the basis price history for the crop elevator and the price history of basis prices paid for the crop commodity by the one or more crop elevators nearby the crop elevator are fed to a second basis prediction machine learned model trained to forecast the basis price for the plurality of future dates. At operation  316 , a root mean square error for the first basis prediction machine learned model and the second basis prediction machine learned model are computed. It should be noted that while root mean square error is one factor, it need not be the only factor and other factors such as mean percentage error can also be used. At operation  318 , these root mean square errors are stored in an application table of an in-memory database. At operation  320 , a selection machine learned model is trained using the stored computed root mean square errors and/or other computed error factors along with the historical trend of a winning model between the first basis price prediction machine learned model and the second basis price prediction machine learned model. At operation  322 , the selection machine learned model is used to automatically select either the first basis prediction machine learned model or the second basis prediction machine learned model. In case the root mean square error difference is small (e.g., within a preset amount) and it is leading to a difference in established trend, the trend may be followed. The root mean square error scores for each model that are stored in operation  318  help in establishing a trend. At operation  324 , a crop earnings forecast is computed by adding the basis price predicted by the selected machine learned model to a forecast crop commodity price and subtracting one or more costs. 
       FIG. 4  is a screen capture illustrating an example user input screen  400  in accordance with an example embodiment. Here, the user is entering a crop rotation plan. Thus, for each of a plurality of different crops, a crop identification box  402  is provided for user entry. Also provided are boxes for a proposed sell date  404 , input cost  406 , expected basis  408  (which may be user entered or system determined after the user enters a location on a different screen), total yield  410 , and farm location  412 . Screen  400  represents an early planning exercise carried out by the farmer to identify the best crop rotation. The screen can be used by the farmer to try alternate crop rotation plans to identify a specific plan which results in the best farm income 
       FIG. 5  is a screen capture illustrating another example user input screen  500  in accordance with another example embodiment. Here, the user is performing crop-pre-planting planning and it may also be used to verify if the ongoing crop rotation plan can be pursued. Boxes for the crop  502 , sell date  504 , input cost  506 , expected basis  508  (which may be user entered or system determined after the user enters a location on a different screen), total yield  510 , and farm location  512  are provided. 
       FIG. 6  is a screen capture illustrating another example user input screen  600  in accordance with another example embodiment. Here, the user is performing best sale date planning at harvest (based on specified harvest date). Boxes for the crop  602 , harvest date  604 , input cost  606 , expected basis  608  (which may be user entered or system determined after the user enters a location on a different screen), total yield  610 , storage rate  612 , and farm location  614  are provided. 
       FIG. 7  is a screen capture illustrating another example user input screen  700  in accordance with another example embodiment. Here, the user is performing best sale date planning post-harvest. Boxes for the crop  702 , sell date  704 , input cost  706 , expected basis  708  (which may be user entered or system determined after the user enters a location on a different screen), total yield  710 , storage rate  712 , and farm location  714  are provided. Since this is post-harvest computation, most likely the farmer already has the crop in storage. So box  706  for input cost may also include storage cost incurred thus far. 
       FIG. 8  is a screen capture illustrating an example output screen  800  in accordance with an example embodiment. Here, a table is depicted. A crop name column  802  may indicate a name of a crop and an elevator address column  804  may indicate an address of corresponding nearby elevator(s) potentially purchasing the crop. A best sale date column  806  may indicate a proposed best sale date for the crop at the corresponding elevator. A futures column  808  may indicate a forecast futures price for a corresponding crop and date in a range of dates. A basis price column  810  may indicate a forecast basis price for the corresponding crop, elevator and date. An input cost column  812  may display an input cost for the corresponding crop and a storage cost column  814  represents storage cost for the corresponding crop and date. 
     A total quantity column  816  may indicate a total yield for each crop. Finally, a crop earning column  818  may indicate the forecast earnings for the crop, corresponding elevator for each date. 
     The range of dates described above may be a range of dates that corresponds with best crop income. For “crop rotation planning” and “crop pre-planting planning” the date range will be around the date requested by the farmer. For other scenarios, the date range is determined by this application. 
     In view of the above described implementations of subject matter this application discloses the following list of examples, wherein one feature of an example in isolation or more than one feature of an example taken in combination and, optionally, in combination with one or more features of one or more further examples, are further examples also falling within the disclosure of this application. 
     EXAMPLES 
     Example 1. A system comprising: 
     at least one hardware processor; and 
     a non-transitory computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising:
         receiving an identification of an item and an identification of a distribution point for the item from a graphical user interface;
           obtaining price history data of prices paid for the item by the distribution point on a plurality of past dates;   feeding the price history data into a first basis prediction machine learned model trained to forecast a basis price for a plurality of future dates based upon the price history data, the basis price being a difference between a national price for the item and a price paid by the distribution point for the item;   obtaining a location for the distribution point;   identifying one or more distribution points nearby the distribution point, based on criteria maintained by a rules framework;   obtaining price history data of prices paid for the item by the one or more distribution points nearby the distribution point;   feeding the price history data for the distribution point and the price history data of prices paid for the item by the one or more distribution points nearby the distribution point to a second basis prediction machine learned model trained to forecast the basis price for the plurality of future dates;   calculating a root mean square error for the first basis prediction machine learned model and the second basis prediction machine learned model and automatically selecting either the first basis prediction machine learned model or the second basis prediction machine learned model based upon which machine learned model has a lowest root mean square error; and   computing an item earnings forecast by adding the basis price predicted by the selected machine learned model to a forecast item price and subtracting one or more costs.
 
Example 2. The system of Example 1, wherein the one or more costs include an input cost received from the graphical user interface.
 
Example 3. The system of Examples 1 or 2, wherein the one or more costs include a storage cost.
 
Example 4. The system of Example 3, wherein the item is a crop commodity and the storage cost is equal to a total yield of the crop commodity multiplied by a storage rate multiplied by a number of days projected for the crop commodity to remain in storage.
 
Example 5. The system of any of Examples 1-4, wherein the operations further comprise:
   
               

     storing the computed root mean square errors in an application table of an in-memory database. 
     Example 6. The system of Example 5, wherein the automatically selecting is performed by a machine learned model trained to use errors of each individual model and the stored computed root mean square errors to identify a trend of a winning model. 
     Example 7. The system of any of Examples 1-6, wherein the operations further comprise: 
     obtaining, via the graphical user interface, a crop rotation plan, the crop rotation plan identifying a plurality of different crop commodities for a single plot of land and, for each of the plurality of different crop commodities, an identified harvest date; and 
     displaying, for each of the plurality of different crop commodities in the crop rotation plan, the computed crop earnings forecast. 
     Example 8. A method comprising: 
     receiving an identification of an item and an identification of a distribution point for the item from a graphical user interface; 
     obtaining price history data of prices paid for the item by the distribution point on a plurality of past dates; 
     feeding the price history data into a first basis prediction machine learned model trained to forecast a basis price for a plurality of future dates based upon the price history data, the basis price being a difference between a national price for the item and a price paid by the distribution point for the item; 
     obtaining a location for the distribution point; 
     identifying one or more distribution points nearby the distribution point, based on criteria maintained by a rules framework; 
     obtaining price history data of prices paid for the item by the one or more distribution points nearby the distribution point; 
     feeding the price history data for the distribution point and the price history data of prices paid for the item by the one or more distribution points nearby the distribution point to a second basis prediction machine learned model trained to forecast the basis price for the plurality of future dates; 
     calculating a root mean square error for the first basis prediction machine learned model and the second basis prediction machine learned model and automatically selecting either the first basis prediction machine learned model or the second basis prediction machine learned model based upon which machine learned model has a lowest root mean square error; and 
     computing an item earnings forecast by adding the basis price predicted by the selected machine learned model to a forecast item price and subtracting one or more costs. 
     Example 9. The method of Example 8, wherein the one or more costs include an input cost received from the graphical user interface. 
     Example 10. The method of Examples 8 or 9, wherein the one or more costs include a storage cost. 
     Example 11. The method of Example 10, wherein the item is a crop commodity and the storage cost is equal to a total yield of the crop commodity multiplied by a storage rate multiplied by a number of days projected for the crop commodity to remain in storage.
 
Example 12. The method of any of Examples 8-11, further comprising:
 
     storing the computed root mean square errors in an application table of an in-memory database. 
     Example 13. The method of Example 12, wherein the automatically selecting is performed by a machine learned model trained to use errors of each individual model and the stored computed root mean square errors to identify a trend of a winning model. 
     Example 14. The method of any of Examples 8-13, wherein the operations further comprise: 
     obtaining, via the graphical user interface, a crop rotation plan, the crop rotation plan identifying a plurality of different crop commodities for a single plot of land and, for each of the plurality of different crop commodities, an identified harvest date; and 
     displaying, for each of the plurality of different crop commodities in the crop rotation plan, the computed crop earnings forecast. 
     Example 15. A non-transitory machine-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising: 
     receiving an identification of an item and an identification of a distribution point for the item from a graphical user interface; 
     obtaining price history data of prices paid for the item by the distribution point on a plurality of past dates; 
     feeding the price history data into a first basis prediction machine learned model trained to forecast a basis price for a plurality of future dates based upon the price history data, the basis price being a difference between a national price for the item and a price paid by the distribution point for the item; 
     obtaining a location for the distribution point; 
     identifying one or more distribution points nearby the distribution point, based on criteria maintained by a rules framework; 
     obtaining price history data of prices paid for the item by the one or more distribution points nearby the distribution point; 
     feeding the price history data for the distribution point and the price history data of prices paid for the item by the one or more distribution points nearby the distribution point to a second basis prediction machine learned model trained to forecast the basis price for the plurality of future dates; 
     calculating a root mean square error for the first basis prediction machine learned model and the second basis prediction machine learned model and automatically selecting either the first basis prediction machine learned model or the second basis prediction machine learned model based upon which machine learned model has a lowest root mean square error; and 
     computing an item earnings forecast by adding the basis price predicted by the selected machine learned model to a forecast item price and subtracting one or more costs. 
     Example 16. The non-transitory machine-readable medium of Example 15, wherein the one or more costs include an input cost received from the graphical user interface. 
     Example 17. The non-transitory machine-readable medium of Examples 15 or 16, wherein the one or more costs include a storage cost. 
     Example 18. The non-transitory machine-readable medium of Example 17, wherein the item is a crop commodity and the storage cost is equal to a total yield of the crop commodity multiplied by a storage rate multiplied by a number of days projected for the crop commodity to remain in storage.
 
Example 19. The non-transitory machine-readable medium of any of Examples 15-18, wherein the operations further comprise:
 
     storing the computed root mean square errors in an application table of an in-memory database. 
     Example 20. The non-transitory machine-readable medium of Example 19, wherein the automatically selecting is performed by a machine learned model trained to use errors of each individual model and the stored computed root mean square errors to identify a trend of a winning model. 
       FIG. 9  is a block diagram  900  illustrating a software architecture  902 , which can be installed on any one or more of the devices described above.  FIG. 9  is merely a non-limiting example of a software architecture, and it will be appreciated that many other architectures can be implemented to facilitate the functionality described herein. In various embodiments, the software architecture  902  is implemented by hardware such as a machine  1000  of  FIG. 10  that includes processors  1010 , memory  1030 , and input/output (I/O) components  1050 . In this example architecture, the software architecture  902  can be conceptualized as a stack of layers where each layer may provide a particular functionality. For example, the software architecture  902  includes layers such as an operating system  904 , libraries  906 , frameworks  908 , and applications  910 . Operationally, the applications  910  invoke API calls  912  through the software stack and receive messages  914  in response to the API calls  912 , consistent with some embodiments. 
     In various implementations, the operating system  904  manages hardware resources and provides common services. The operating system  904  includes, for example, a kernel  920 , services  922 , and drivers  924 . The kernel  920  acts as an abstraction layer between the hardware and the other software layers, consistent with some embodiments. For example, the kernel  920  provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionality. The services  922  can provide other common services for the other software layers. The drivers  924  are responsible for controlling or interfacing with the underlying hardware, according to some embodiments. For instance, the drivers  924  can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low-Energy drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audio drivers, power management drivers, and so forth. 
     In some embodiments, the libraries  906  provide a low-level common infrastructure utilized by the applications  910 . The libraries  906  can include system libraries  930  (e.g., C standard library) that can provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries  906  can include API libraries  932  such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in 2D and 3D in a graphic context on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries  906  can also include a wide variety of other libraries  934  to provide many other APIs to the applications  910 . 
     The frameworks  908  provide a high-level common infrastructure that can be utilized by the applications  910 , according to some embodiments. For example, the frameworks  908  provide various graphical user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks  908  can provide a broad spectrum of other APIs that can be utilized by the applications  910 , some of which may be specific to a particular operating system  904  or platform. 
     In an example embodiment, the applications  910  include a home application  950 , a contacts application  952 , a browser application  954 , a book reader application  956 , a location application  958 , a media application  960 , a messaging application  962 , a game application  964 , and a broad assortment of other applications, such as a third-party application  966 . According to some embodiments, the applications  910  are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications  910 , structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application  966  (e.g., an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party application  966  can invoke the API calls  912  provided by the operating system  904  to facilitate functionality described herein. 
       FIG. 10  illustrates a diagrammatic representation of a machine  1000  in the form of a computer system within which a set of instructions may be executed for causing the machine  1000  to perform any one or more of the methodologies discussed herein, according to an example embodiment. Specifically,  FIG. 10  shows a diagrammatic representation of the machine  1000  in the example form of a computer system, within which instructions  1016  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  1000  to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions  1016  may cause the machine  1000  to execute the methods of  FIG. 3 . Additionally, or alternatively, the instructions  1016  may implement  FIGS. 1-8  and so forth. The instructions  1016  transform the general, non-programmed machine  1000  into a particular machine  1000  programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine  1000  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  1000  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine  1000  may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  1016 , sequentially or otherwise, that specify actions to be taken by the machine  1000 . Further, while only a single machine  1000  is illustrated, the term “machine” shall also be taken to include a collection of machines  1000  that individually or jointly execute the instructions  1016  to perform any one or more of the methodologies discussed herein. 
     The machine  1000  may include processors  1010 , memory  1030 , and I/O components  1050 , which may be configured to communicate with each other such as via a bus  1002 . In an example embodiment, the processors  1010  (e.g., a central processing unit (CPU), a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a graphics processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a radio-frequency integrated circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor  1012  and a processor  1014  that may execute the instructions  1016 . The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions  1016  contemporaneously. Although  FIG. 10  shows multiple processors  1010 , the machine  1000  may include a single processor  1012  with a single core, a single processor  1012  with multiple cores (e.g., a multi-core processor  1012 ), multiple processors  1012 ,  1014  with a single core, multiple processors  1012 ,  1014  with multiple cores, or any combination thereof. 
     The memory  1030  may include a main memory  1032 , a static memory  1034 , and a storage unit  1036 , each accessible to the processors  1010  such as via the bus  1002 . The main memory  1032 , the static memory  1034 , and the storage unit  1036  store the instructions  1016  embodying any one or more of the methodologies or functions described herein. The instructions  1016  may also reside, completely or partially, within the main memory  1032 , within the static memory  1034 , within the storage unit  1036 , within at least one of the processors  1010  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  1000 . 
     The I/O components  1050  may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components  1050  that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components  1050  may include many other components that are not shown in  FIG. 10 . The I/O components  1050  are grouped according to functionality merely for simplifying the following discussion, and the grouping is in no way limiting. In various example embodiments, the I/O components  1050  may include output components  1052  and input components  1054 . The output components  1052  may include visual components (e.g., a display such as a plasma display panel (PDP), a light-emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components  1054  may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. 
     In further example embodiments, the I/O components  1050  may include biometric components  1056 , motion components  1058 , environmental components  1060 , or position components  1062 , among a wide array of other components. For example, the biometric components  1056  may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components  1058  may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. 
     The environmental components  1060  may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detect concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components  1062  may include location sensor components (e.g., a Global Positioning System (GPS) receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like. 
     Communication may be implemented using a wide variety of technologies. The I/O components  1050  may include communication components  1064  operable to couple the machine  1000  to a network  1080  or devices  1070  via a coupling  1082  and a coupling  1072 , respectively. For example, the communication components  1064  may include a network interface component or another suitable device to interface with the network  1080 . In further examples, the communication components  1064  may include wired communication components, wireless communication components, cellular communication components, near field communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices  1070  may be another machine or any of a wide variety of peripheral devices (e.g., coupled via a USB). 
     Moreover, the communication components  1064  may detect identifiers or include components operable to detect identifiers. For example, the communication components  1064  may include radio-frequency identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as QR code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components  1064 , such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth. 
     The various memories (i.e.,  1030 ,  1032 ,  1034 , and/or memory of the processor(s)  1010 ) and/or the storage unit  1036  may store one or more sets of instructions  1016  and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions  1016 ), when executed by the processor(s)  1010 , cause various operations to implement the disclosed embodiments. 
     As used herein, the terms “machine-storage medium,” “device-storage medium,” and “computer-storage medium” mean the same thing and may be used interchangeably. The terms refer to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions and/or data. The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media, and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), field-programmable gate array (FPGA), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below. 
     In various example embodiments, one or more portions of the network  1080  may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local-area network (LAN), a wireless LAN (WLAN), a wide-area network (WAN), a wireless WAN (WWAN), a metropolitan-area network (MAN), the Internet, a portion of the Internet, a portion of the public switched telephone network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network  1080  or a portion of the network  1080  may include a wireless or cellular network, and the coupling  1082  may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, the coupling  1082  may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long-Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long-range protocols, or other data transfer technology. 
     The instructions  1016  may be transmitted or received over the network  1080  using a transmission medium via a network interface device (e.g., a network interface component included in the communication components  1064 ) and utilizing any one of a number of well-known transfer protocols (e.g., HTTP). Similarly, the instructions  1016  may be transmitted or received using a transmission medium via the coupling  1072  (e.g., a peer-to-peer coupling) to the devices  1070 . The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure. The terms “transmission medium” and “signal medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions  1016  for execution by the machine  1000 , and include digital or analog communications signals or other intangible media to facilitate communication of such software. Hence, the terms “transmission medium” and “signal medium” shall be taken to include any form of modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. 
     The terms “machine-readable medium,” “computer-readable medium,” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals.