SYSTEMS AND METHODS FOR IMPROVING FORECASTING MODELS
The disclosure relates to a method for creating an improved forecasting model. The method includes identifying a plurality of drivers affecting actual sales or demand of a product or service; receiving a respective data set for each of the drivers; generating one or more lagged data sets for each driver by applying lags to the data set associated with the driver; for each driver: forming a group of data sets by grouping the data set associated with the driver with the lagged data sets for the driver; and selecting a data set in the group of data sets that best correlates with sales or demand changes of the product or service; determining which one or more of the selected data sets for the drivers increases forecasting accuracy for sales or demand of the product or service; and training a forecasting model using the one or more selected data sets.
1 . A method for creating an improved forecasting model, the method comprising:
identifying a plurality of drivers affecting actual sales or demand of a product or service;
receiving a respective data set for each of the drivers;
generating one or more lagged data sets for each driver by applying one or more lags to the data set associated with the driver;
for each driver:
forming a group of data sets by grouping the data set associated with the driver with the one or more lagged data sets for the driver; and
selecting a data set in the group of data sets that best correlates with sales or demand changes of the product or service;
determining which one or more of the selected data sets for the drivers increases forecasting accuracy for sales or demand of the product or service; and
training a forecasting model using the one or more selected data sets.
2 . The method of claim 1 , further comprising modifying the data set associated with at least one of the drivers to compensate for one or more rare events.
3 . The method of claim 1 , wherein the plurality of drivers comprises one or more social media drivers and one or more non-social media drivers.
4 . The method of claim 1 , further comprising calculating error metric values for the forecasting model.
5 . The method of claim 4 , wherein the error metric values include mean absolute percentage error and weighted average percentage error.
6 . The method of claim 1 , further comprising identifying the respective data set for each of the drivers based on a keywords dictionary before applying one or more lags to the data set, the keywords dictionary including misspelled words and shortened words for each of the drivers.
7 . The method of claim 1 , wherein the forecasting model is used to predict demand based on new input data.
8 . The method of claim 1 , further comprising cleansing the respective data set for each of the drivers before applying one or more lags to the data set.
9 . A system for creating an improved forecasting model, the system comprising:
a memory; and
one or more processors coupled with the memory, wherein the one or more processors, when executed, perform operations comprising:
identifying a plurality of drivers affecting actual sales or demand of a product or service;
receiving a respective data set for each of the drivers;
generating one or more lagged data sets for each driver by applying one or more lags to the data set associated with the driver;
for each driver:
forming a group of data sets by grouping the data set associated with the driver with the one or more lagged data sets for the driver; and
selecting a data set in the group of data sets that best correlates with sales or demand changes of the product or service;
determining which one or more of the selected data sets for the drivers increases forecasting accuracy for sales or demand of the product or service; and
training a forecasting model using the one or more selected data sets.
10 . The system of claim 9 , wherein the operations further comprise modifying the data set associated with at least one of the drivers to compensate for one or more rare events.
11 . The system of claim 9 , wherein the plurality of drivers comprises one or more social media drivers and one or more non-social media drivers.
12 . The system of claim 9 , wherein the operations further comprise calculating error metric values for the forecasting model.
13 . The system of claim 12 , wherein the error metric values include mean absolute percentage error and weighted average percentage error.
14 . The system of claim 9 , wherein the operations further comprise identifying the respective data set for each of the drivers based on a keywords dictionary before applying one or more lags to the data set, the keywords dictionary including misspelled words and shortened words for each of the drivers.
15 . The system of claim 9 , wherein the forecasting model is used to predict demand based on new input data.
16 . The system of claim 9 , wherein the operations further comprise cleansing the respective data set for each of the drivers before applying one or more lags to the data set.
17 . A non-transitory computer readable medium containing computer-readable instructions stored therein for causing a computer processor to perform operations comprising:
identifying a plurality of drivers affecting actual sales or demand of a product or service;
receiving a respective data set for each of the drivers;
generating one or more lagged data sets for each driver by applying one or more lags to the data set associated with the driver;
for each driver:
forming a group of data sets by grouping the data set associated with the driver with the one or more lagged data sets for the driver; and
selecting a data set in the group of data sets that best correlates with sales or demand changes of the product or service;
determining which one or more of the selected data sets for the drivers increases forecasting accuracy for sales or demand of the product or service; and
training a forecasting model using the one or more selected data sets.