Systems and methods for estimating stability of a dataset

1 . A computer-implemented method, comprising:

measuring a set of payments and balances associated with one or more accounts corresponding to a customer, wherein the set of payments and balances is measured over a period of time through input data obtained from a set of data sources;

processing the set of payments and balances through a non-parametric algorithm to obtain rates of change corresponding to the set of payments and balances, wherein the rates of change are obtained as the input data is obtained;

integrating the rates of change into a set of vectors, wherein the set of vectors includes different values corresponding to the input data;

processing the set of vectors through a mapping algorithm to generate a mapping of the set of vectors to a sequence of template states, wherein the mapping algorithm is trained using training data that includes a set of input vectors without any target values, and wherein the mapping algorithm generates the mapping according to a dictionary of template states generated by the mapping algorithm by identifying different clusters from the training data;

processing the mapping of the set of vectors to the sequence of template states over the period of time to generate a time series of the sequence of template states;

processing the time series through a classification algorithm to generate a set of features associated with the time series, wherein the classification algorithm uses a similarity measure to identify the set of features according to a proximity to the sequence of template states;

processing the set of features through a predictive model to identify a set of predictive characteristics associated with the customer, wherein the predictive model is trained using a dataset that includes a set of known trends in different sequences of template states;

generating an offer for a new account based on the set of predictive characteristics; and

updating the predictive model according to feedback associated with the set of predictive characteristics and the offer.

2 . The computer-implemented method of claim 1 , wherein the predictive model identifies the set of predictive characteristics by comparing the sequence of template states to various historical analyses of different trends observed over the period of time.

3 . The computer-implemented method of claim 1 , wherein the mapping algorithm generates the mapping as a result of the set of vectors being directionally similar to the sequence of template states according to cosine similarity.

4 . The computer-implemented method of claim 1 , wherein the mapping algorithm generates the mapping according to a normalized Euclidean distance between the set of vectors and clusters corresponding to the sequence of template states.

5 . The computer-implemented method of claim 1 , wherein the set of data sources includes one or more public repositories, credit reporting agencies, billing services, and medical services.

6 . The computer-implemented method of claim 1 , wherein generating the set of features further includes performing data mining on the time series.

7 . The computer-implemented method of claim 1 , wherein a cluster from the different clusters corresponds to a particular template state, and wherein the particular template state corresponds to a stability and polarity of members of the cluster according to expected future stability.

8 . A system, comprising:

one or more processors; and

memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:

measure a set of payments and balances associated with one or more accounts corresponding to a customer, wherein the set of payments and balances is measured over a period of time through input data obtained from a set of data sources;

process the set of payments and balances through a non-parametric algorithm to obtain rates of change corresponding to the set of payments and balances, wherein the rates of change are obtained as the input data is obtained;

integrate the rates of change into a set of vectors, wherein the set of vectors includes different values corresponding to the input data;

process the set of vectors through a mapping algorithm to generate a mapping of the set of vectors to a sequence of template states, wherein the mapping algorithm is trained using training data that includes a set of input vectors without any target values, and wherein the mapping algorithm generates the mapping according to a dictionary of template states generated by the mapping algorithm by identifying different clusters from the training data;

process the mapping of the set of vectors to the sequence of template states over the period of time to generate a time series of the sequence of template states;

process the time series through a classification algorithm to generate a set of features associated with the time series, wherein the classification algorithm uses a similarity measure to identify the set of features according to a proximity to the sequence of template states;

process the set of features through a predictive model to identify a set of predictive characteristics associated with the customer, wherein the predictive model is trained using a dataset that includes a set of known trends in different sequences of template states;

generate an offer for a new account based on the set of predictive characteristics; and

update the predictive model according to feedback associated with the set of predictive characteristics and the offer.

9 . The system of claim 8 , wherein the predictive model identifies the set of predictive characteristics by comparing the sequence of template states to various historical analyses of different trends observed over the period of time.

10 . The system of claim 8 , wherein the mapping algorithm generates the mapping as a result of the set of vectors being directionally similar to the sequence of template states according to cosine similarity.

11 . The system of claim 8 , wherein the mapping algorithm generates the mapping according to a normalized Euclidean distance between the set of vectors and clusters corresponding to the sequence of template states.

12 . The system of claim 8 , wherein the set of data sources includes one or more public repositories, credit reporting agencies, billing services, and medical services.

13 . The system of claim 8 , wherein the instructions that cause the system to generate the set of features further cause the system to perform data mining on the time series.

14 . The system of claim 8 , wherein a cluster from the different clusters corresponds to a particular template state, and wherein the particular template state corresponds to a stability and polarity of members of the cluster according to expected future stability.

15 . A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:

measure a set of payments and balances associated with one or more accounts corresponding to a customer, wherein the set of payments and balances is measured over a period of time through input data obtained from a set of data sources;

process the set of payments and balances through a non-parametric algorithm to obtain rates of change corresponding to the set of payments and balances, wherein the rates of change are obtained as the input data is obtained;

integrate the rates of change into a set of vectors, wherein the set of vectors includes different values corresponding to the input data;

process the set of vectors through a mapping algorithm to generate a mapping of the set of vectors to a sequence of template states, wherein the mapping algorithm is trained using training data that includes a set of input vectors without any target values, and wherein the mapping algorithm generates the mapping according to a dictionary of template states generated by the mapping algorithm by identifying different clusters from the training data;

process the mapping of the set of vectors to the sequence of template states over the period of time to generate a time series of the sequence of template states;

process the time series through a classification algorithm to generate a set of features associated with the time series, wherein the classification algorithm uses a similarity measure to identify the set of features according to a proximity to the sequence of template states;

process the set of features through a predictive model to identify a set of predictive characteristics associated with the customer, wherein the predictive model is trained using a dataset that includes a set of known trends in different sequences of template states;

generate an offer for a new account based on the set of predictive characteristics; and

update the predictive model according to feedback associated with the set of predictive characteristics and the offer.

16 . The non-transitory, computer-readable storage medium of claim 15 , wherein the predictive model identifies the set of predictive characteristics by comparing the sequence of template states to various historical analyses of different trends observed over the period of time.

17 . The non-transitory, computer-readable storage medium of claim 15 , the mapping algorithm generates the mapping as a result of the set of vectors being directionally similar to the sequence of template states according to cosine similarity.

18 . The non-transitory, computer-readable storage medium of claim 15 , wherein the mapping algorithm generates the mapping according to a normalized Euclidean distance between the set of vectors and clusters corresponding to the sequence of template states.

19 . The non-transitory, computer-readable storage medium of claim 15 , wherein the set of data sources includes one or more public repositories, credit reporting agencies, billing services, and medical services.

20 . The non-transitory, computer-readable storage medium of claim 15 , wherein the executable instructions that cause the computer system to generate the set of features further cause the computer system to perform data mining on the time series.

21 . The non-transitory, computer-readable storage medium of claim 15 , wherein a cluster from the different clusters corresponds to a particular template state, and wherein the particular template state corresponds to a stability and polarity of members of the cluster according to expected future stability.