Resource-related forecasting using machine learning techniques
Methods, apparatus, and processor-readable storage media for resource-related forecasting using machine learning techniques are provided herein. An example computer-implemented method includes obtaining multiple items of data related to one or more resources associated with an enterprise; correlating at least a portion of the multiple items of data with at least one target variable using one or more correlation techniques; generating one or more forecasts pertaining to the at least one target variable and at least a portion of the one or more resources by processing at least a portion of the correlated data using one or more machine learning techniques; and performing one or more automated actions based at least in part on the one or more forecasts.
1 . A computer-implemented method comprising:
obtaining multiple items of data related to one or more resources associated with an enterprise;
processing at least a plurality of the multiple items of data into at least one structured query language server;
correlating at least a portion of the multiple items of data within the at least one structured query language server with at least one target variable using one or more correlation techniques, wherein correlating the at least a portion of the multiple items of data with at least one target variable comprises calculating at least one rank correlation between temporally based differenced values derived from the multiple items of data and the at least one target variable;
automatically training at least one random forest model and at least one linear regression model based at least in part on the correlated portion of the multiple items of data associated with correlation values above at least one designated threshold, wherein automatically training the at least one random forest model comprises automatically learning model parameters comprising at least one number of samples in at least one node of at least one decision tree, depth of the at least one decision tree, at least one number of random features to associate with the at least one node, and at least one number of trees to be built in one or more random forests;
generating one or more forecasts pertaining to the at least one target variable and at least a portion of the one or more resources by processing input data related to the at least a portion of the one or more resources using the at least one trained random forest model and the at least one trained linear regression model; and
performing one or more automated actions based at least in part on the one or more forecasts, wherein performing the one or more automated actions comprises automatically deploying, in accordance with the one or more forecasts, at least a portion of the one or more resources, wherein the at least a portion of the one or more resources comprises one or more hardware devices;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
2 . The computer-implemented method of claim 1 , wherein performing the one or more automated actions comprises automatically allocating, in accordance with the one or more forecasts, at least a portion of the one or more resources in connection with one or more systems.
3 . The computer-implemented method of claim 1 , wherein correlating the at least a portion of the multiple items of data with at least one target variable comprises calculating at least one Spearman's rank correlation between the at least a portion of the multiple items of data and the at least one target variable.
4 . The computer-implemented method of claim 1 , wherein calculating at least one rank correlation between temporally based differenced values derived from the multiple items of data and the at least one target variable comprises calculating at least one Spearman's rank correlation between the temporally based differenced values derived from the multiple items of data and the at least one target variable.
5 . The computer-implemented method of claim 1 , wherein correlating the at least a portion of the multiple items of data with at least one target variable comprises calculating at least one Spearman's rank correlation between geographically based data derived from the multiple items of data and the at least one target variable.
6 . The computer-implemented method of claim 1 , wherein performing the one or more automated actions comprises automatically allocating, in connection with one or more systems, at least one resource related to at least a portion of the one or more resources associated with the one or more forecasts.
7 . The computer-implemented method of claim 1 , wherein performing the one or more automated actions comprises automatically training at least a portion of the at least one random forest model and the at least one linear regression model using feedback related to at least a portion of the one or more forecasts.
8 . The computer-implemented method of claim 1 , wherein obtaining the multiple items of data related to one or more resources comprises obtaining multiple items of time series data related to one or more categories of hardware devices.
9 . A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:
to obtain multiple items of data related to one or more resources associated with an enterprise;
to process at least a plurality of the multiple items of data into at least one structured query language server;
to correlate at least a portion of the multiple items of data within the at least one structured query language server with at least one target variable using one or more correlation techniques, wherein correlating the at least a portion of the multiple items of data with at least one target variable comprises calculating at least one rank correlation between temporally based differenced values derived from the multiple items of data and the at least one target variable;
to automatically train at least one random forest model and at least one linear regression model based at least in part on the correlated portion of the multiple items of data associated with correlation values above at least one designated threshold, wherein automatically training the at least one random forest model comprises automatically learning model parameters comprising at least one number of samples in at least one node of at least one decision tree, depth of the at least one decision tree, at least one number of random features to associate with the at least one node, and at least one number of trees to be built in one or more random forests;
to generate one or more forecasts pertaining to the at least one target variable and at least a portion of the one or more resources by processing input data related to the at least a portion of the one or more resources using the at least one trained random forest model and the at least one trained linear regression model; and
to perform one or more automated actions based at least in part on the one or more forecasts, wherein performing the one or more automated actions comprises automatically deploying, in accordance with the one or more forecasts, at least a portion of the one or more resources, wherein the at least a portion of the one or more resources comprises one or more hardware devices.
10 . The non-transitory processor-readable storage medium of claim 9 , wherein performing the one or more automated actions comprises automatically allocating, in accordance with the one or more forecasts, at least a portion of the one or more resources in connection with one or more systems.
11 . The non-transitory processor-readable storage medium of claim 9 , wherein correlating the at least a portion of the multiple items of data with at least one target variable comprises calculating at least one Spearman's rank correlation between the at least a portion of the multiple items of data and the at least one target variable.
12 . The non-transitory processor-readable storage medium of claim 9 , wherein calculating at least one rank correlation between temporally based differenced values derived from the multiple items of data and the at least one target variable comprises calculating at least one Spearman's rank correlation between the temporally based differenced values derived from the multiple items of data and the at least one target variable.
13 . The non-transitory processor-readable storage medium of claim 9 , wherein correlating the at least a portion of the multiple items of data with at least one target variable comprises calculating at least one Spearman's rank correlation between geographically based data derived from the multiple items of data and the at least one target variable.
14 . The non-transitory processor-readable storage medium of claim 9 , wherein performing the one or more automated actions comprises automatically training at least a portion of the at least one random forest model and the at least one linear regression model using feedback related to at least a portion of the one or more forecasts.
15 . An apparatus comprising:
at least one processing device comprising a processor coupled to a memory;
the at least one processing device being configured:
to obtain multiple items of data related to one or more resources associated with an enterprise;
to process at least a plurality of the multiple items of data into at least one structured query language server;
to correlate at least a portion of the multiple items of data within the at least one structured query language server with at least one target variable using one or more correlation techniques, wherein correlating the at least a portion of the multiple items of data with at least one target variable comprises calculating at least one rank correlation between temporally based differenced values derived from the multiple items of data and the at least one target variable;
to automatically train at least one random forest model and at least one linear regression model based at least in part on the correlated portion of the multiple items of data associated with correlation values above at least one designated threshold, wherein automatically training the at least one random forest model comprises automatically learning model parameters comprising at least one number of samples in at least one node of at least one decision tree, depth of the at least one decision tree, at least one number of random features to associate with the at least one node, and at least one number of trees to be built in one or more random forests;
to generate one or more forecasts pertaining to the at least one target variable and at least a portion of the one or more resources by processing input data related to the at least a portion of the one or more resources using the at least one trained random forest model and the at least one trained linear regression model; and
to perform one or more automated actions based at least in part on the one or more forecasts, wherein performing the one or more automated actions comprises automatically deploying, in accordance with the one or more forecasts, at least a portion of the one or more resources, wherein the at least a portion of the one or more resources comprises one or more hardware devices.
16 . The apparatus of claim 15 , wherein performing the one or more automated actions comprises automatically allocating, in accordance with the one or more forecasts, at least a portion of the one or more resources in connection with one or more systems.
17 . The apparatus of claim 15 , wherein correlating the at least a portion of the multiple items of data with at least one target variable comprises calculating at least one Spearman's rank correlation between the at least a portion of the multiple items of data and the at least one target variable.
18 . The apparatus of claim 15 , wherein calculating at least one rank correlation between temporally based differenced values derived from the multiple items of data and the at least one target variable comprises calculating at least one Spearman's rank correlation between the temporally based differenced values derived from the multiple items of data and the at least one target variable.
19 . The apparatus of claim 15 , wherein correlating the at least a portion of the multiple items of data with at least one target variable comprises calculating at least one Spearman's rank correlation between geographically based data derived from the multiple items of data and the at least one target variable.
20 . The apparatus of claim 15 , wherein performing the one or more automated actions comprises automatically training at least a portion of the at least one random forest model and the at least one linear regression model using feedback related to at least a portion of the one or more forecasts.