Abstract: An advanced data platform may receive an asset pricing request containing information about an asset. An optimization engine may determine a predicted price for the asset at different locations and times and compute a price matrix accordingly. The engine may identify an optimized predicted price from the price matrix, taking into account the spatial and temporal factors and various optimization conditions. A view for presentation of the optimized predicted price for the asset on a client device is generated and communicated to the client device over a network. When the asset is a vehicle, the engine may compute a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising input variables representing attributes describing the vehicle.

Abstract: A residual value forecasting system may utilize heterogeneous data, such as used market data, industry-specific data, and non-industry-specific data, from disparate data sources to produce residual value forecasts of an item based on a sophisticated residual value forecasting model particularly configured for agility. The system can dynamically and quickly adapt to change in data inputs and produce custom outputs. The system may determine a baseline value for an item using the used market data, a microeconomic factor using the industry-specific data, and a macroeconomic factor using the non-industry-specific data, as well as adjustments such as locality adjustments and modifications. Given the macroeconomic factor and the microeconomic factor relative to the locality-adjusted value of the item and in view of the competitive sets of similar and/or substitute items in the same industry, the system can generate an accurate forecast residual value of the item at a future time point.

Abstract: An advanced data platform may receive an asset pricing request containing information about an asset. An optimization engine may determine a predicted price for the asset at different locations and times and compute a price matrix accordingly. The engine may identify an optimized predicted price from the price matrix, taking into account the spatial and temporal factors and various optimization conditions. A view for presentation of the optimized predicted price for the asset on a client device is generated and communicated to the client device over a network. When the asset is a vehicle, the engine may compute a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising input variables representing attributes describing the vehicle.

Abstract: An advanced data platform may receive an asset pricing request containing information about an asset. An optimization engine may determine a predicted price for the asset at different locations and times and compute a price matrix accordingly. The engine may identify an optimized predicted price from the price matrix, taking into account the spatial and temporal factors and various optimization conditions. A view for presentation of the optimized predicted price for the asset on a client device is generated and communicated to the client device over a network. When the asset is a vehicle, the engine may compute a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising input variables representing attributes describing the vehicle.

Abstract: A residual value forecasting system may utilize heterogeneous data, such as used market data, industry-specific data, and non-industry-specific data, from disparate data sources to produce residual value forecasts of an item based on a sophisticated residual value forecasting model particularly configured for agility. The system can dynamically and quickly adapt to change in data inputs and produce custom outputs. The system may determine a baseline value for an item using the used market data, a microeconomic factor using the industry-specific data, and a macroeconomic factor using the non-industry-specific data, as well as adjustments such as locality adjustments and modifications. Given the macroeconomic factor and the microeconomic factor relative to the locality-adjusted value of the item and in view of the competitive sets of similar and/or substitute items in the same industry, the system can generate an accurate forecast residual value of the item at a future time point.

Abstract: A residual value forecasting system may utilize heterogeneous data, such as used market data, industry-specific data, and non-industry-specific data, from disparate data sources to produce residual value forecasts of an item based on a sophisticated residual value forecasting model particularly configured for agility. The system can dynamically and quickly adapt to change in data inputs and produce custom outputs. The system may determine a baseline value for an item using the used market data, a microeconomic factor using the industry-specific data, and a macroeconomic factor using the non-industry-specific data, as well as adjustments such as locality adjustments and modifications. Given the macroeconomic factor and the microeconomic factor relative to the locality-adjusted value of the item and in view of the competitive sets of similar and/or substitute items in the same industry, the system can generate an accurate forecast residual value of the item at a future time point.

Abstract: Systems, methods and computer program products for forecasting future values of an item, where an initial value for the item is determined, and then a baseline forecast for a future reference period is computed based on factors that include microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item. The forecast may also be adjusted based on data for a set of competitive items. The forecast for the item is stored and is then made available to clients that can access the forecast to determine the expected future value of the item at some point in the future.

Abstract: An advanced data platform may receive an asset pricing request containing information about an asset. An optimization engine may determine a predicted price for the asset at different locations and times and compute a price matrix accordingly. The engine may identify an optimized predicted price from the price matrix, taking into account the spatial and temporal factors and various optimization conditions. A view for presentation of the optimized predicted price for the asset on a client device is generated and communicated to the client device over a network. When the asset is a vehicle, the engine may compute a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising input variables representing attributes describing the vehicle.

Abstract: A residual value forecasting system may utilize heterogeneous data, such as used market data, industry-specific data, and non-industry-specific data, from disparate data sources to produce residual value forecasts of an item based on a sophisticated residual value forecasting model particularly configured for agility. The system can dynamically and quickly adapt to change in data inputs and produce custom outputs. The system may determine a baseline value for an item using the used market data, a microeconomic factor using the industry-specific data, and a macroeconomic factor using the non-industry-specific data, as well as adjustments such as locality adjustments and modifications. Given the macroeconomic factor and the microeconomic factor relative to the locality-adjusted value of the item and in view of the competitive sets of similar and/or substitute items in the same industry, the system can generate an accurate forecast residual value of the item at a future time point.

Abstract: Systems, methods and computer program products for forecasting future values of an item, where an initial value for the item is determined, and then a baseline forecast for a future reference period is computed based on factors that include microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item. The forecast may also be adjusted based on data for a set of competitive items. The forecast for the item is stored and is then made available to clients that can access the forecast to determine the expected future value of the item at some point in the future.

Abstract: An advanced data platform may receive an asset pricing request containing information about an asset. An optimization engine may determine a predicted price for the asset at different locations and times and compute a price matrix accordingly. The engine may identify an optimized predicted price from the price matrix, taking into account the spatial and temporal factors and various optimization conditions. A view for presentation of the optimized predicted price for the asset on a client device is generated and communicated to the client device over a network. When the asset is a vehicle, the engine may compute a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising input variables representing attributes describing the vehicle.

Abstract: A residual value forecasting system may utilize heterogeneous data, such as used market data, industry-specific data, and non-industry-specific data, from disparate data sources to produce residual value forecasts of an item based on a sophisticated residual value forecasting model particularly configured for agility. The system can dynamically and quickly adapt to change in data inputs and produce custom outputs. The system may determine a baseline value for an item using the used market data, a microeconomic factor using the industry-specific data, and a macroeconomic factor using the non-industry-specific data, as well as adjustments such as locality adjustments and modifications. Given the macroeconomic factor and the microeconomic factor relative to the locality-adjusted value of the item and in view of the competitive sets of similar and/or substitute items in the same industry, the system can generate an accurate forecast residual value of the item at a future time point.

Abstract: Systems, methods and computer program products for forecasting future values of an item, where an initial value for the item is determined, and then a baseline forecast for a future reference period is computed based on factors that include microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item. The forecast may also be adjusted based on data for a set of competitive items. The forecast for the item is stored and is then made available to clients that can access the forecast to determine the expected future value of the item at some point in the future.

Abstract: A residual value forecasting system may utilize heterogeneous data, such as used market data, industry-specific data, and non-industry-specific data, from disparate data sources to produce residual value forecasts of an item based on a sophisticated residual value forecasting model particularly configured for agility. The system can dynamically and quickly adapt to change in data inputs and produce custom outputs. The system may determine a baseline value for an item using the used market data, a microeconomic factor using the industry-specific data, and a macroeconomic factor using the non-industry-specific data, as well as adjustments such as locality adjustments and modifications. Given the macroeconomic factor and the microeconomic factor relative to the locally adjusted value of the item and in view of the competitive sets of similar and/or substitute items in the same industry, the system can generate an accurate forecast residual value of the item at a future time point.

Abstract: Systems, methods and computer program products for forecasting future values of an item, where an initial value for the item is determined, and then a baseline forecast for a future reference period is computed based on factors that include microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item. The forecast may also be adjusted based on data for a set of competitive items. The forecast for the item is stored and is then made available to clients that can access the forecast to determine the expected future value of the item at some point in the future.

Abstract: A filtering face mask includes a top layer, a structure layer, one or more filtering layers, a back layer and straps. The straps, the ends of which are attached to the mask, run through strap holes in the mask, and may be pulled to change the mask from a flat to a wearable configuration. The unpleated top layer may easily be decorated before wearing the mask.

Abstract: A disposable filter cup has an open-bottom cup, a permeable lower cross-sectional barrier adhered to the interior wall of the cup, a permeable upper cross-sectional barrier adhered to the interior wall of the cup between the lower cross-sectional barrier and the top of the cup, a chamber defined by a volume between the upper cross-sectional barrier and the top of the cup, and a plurality of arms extending from an exterior wall of the cup. In an alternative embodiment, one or more inverted, generally U-shaped channels extend outwardly from a rim of the cup. The arms and/or the one or more U-shaped channels engage with a rim of a drinking receptacle to provide support for the filter cup within the drinking receptacle. A brewing medium is contained between the lower cross-sectional barrier and the upper cross-sectional barrier.

Abstract: Systems, methods and computer program products for forecasting future values of an item, where an initial value for the item is determined, and then a baseline forecast for a future reference period is computed based on factors that include microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item. The forecast may also be adjusted based on data for a set of competitive items. The forecast for the item is stored and is then made available to clients that can access the forecast to determine the expected future value of the item at some point in the future.

Abstract: An advanced data platform may receive an asset pricing request containing information about an asset. An optimization engine may determine a predicted price for the asset at different locations and times and compute a price matrix accordingly. The engine may identify an optimized predicted price from the price matrix, taking into account the spatial and temporal factors and various optimization conditions. A view for presentation of the optimized predicted price for the asset on a client device is generated and communicated to the client device over a network. When the asset is a vehicle, the engine may compute a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising input variables representing attributes describing the vehicle.

Abstract: Systems, methods and products for determining residual values of asset portfolios are disclosed. In one embodiment, a system includes a server computer coupled to a network and to a data storage device. The server generates residual value curves for each of a set of item types and stores them. The server computer also receives and/or maintains information defining a set of items in a portfolio. When an assessment of the portfolio is initiated, the server determines a residual value for each item in the portfolio by identifying a corresponding one of the item types, retrieving the residual value curve corresponding to the identified item type, and determining a future value of the item based on the retrieved residual value curve for the corresponding item type. The server then aggregates the individual residual values into a residual value for the portfolio and enables access to this value.