A processing unit can acquire datasets from respective data sources, each having a respective unique data domain. The processing unit can determine values of a plurality of features based on the plurality of datasets. The processing unit can modify input-specific parameters or history parameters of a computational model based on the values of the features. In some examples, the processing unit can determine an estimated value of a target feature based at least in part on the modified computational model and values of one or more reference features. In some examples, the computational model can include neural networks for several input sets. An output layer of at least one of the neural networks can be connected to the respective hidden layer(s) of one or more other(s) of the neural networks. In some examples, the neural networks can be operated to provide transformed feature value(s) for respective times.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: one or more computer-readable media having thereon: a plurality of modules; and a computational model comprising respective input-specific parameters for each of a plurality of input sets of the computational model and one or more history parameters; and one or more processing unit(s) operably coupled to one(s) of the computer-readable media and communicatively connectable with a plurality of data sources, each of the data sources having a respective unique data domain and the processing unit(s) adapted to execute modules of the plurality of modules comprising: an accessing module configured to acquire a plurality of datasets from respective ones of the data sources, wherein the data in each dataset is organized according to the respective data domain; an extraction module configured to determine values of a plurality of features based at least in part on the plurality of datasets; and an updating module configured to modify at least some of the input-specific parameters or the history parameters of the computational model based at least in part on the values of the plurality of features. 2. A system as claim 1 recites, wherein: the plurality of features includes a target feature and one or more reference features; and the updating module is configured to modify the at least some of the input-specific parameters or the history parameters of the computational model based at least in part on values of the target feature and values of the one or more reference features. 3. A system as claim 2 recites, wherein the plurality of modules further comprises: an estimating module configured to determine an estimated value of the target feature based at least in part on the modified computational model and values of the one or more reference features, wherein the estimating module provides at least some of the values of the one or more reference features in each of the input sets of the computational model. 4. A system as claim 3 recites, wherein the estimated value of the target feature corresponds to a time subsequent to a time period of the values of the one or more reference features. 5. A system as claim 3 recites, wherein: the computational model includes a first neural network and a second neural network having respective input layers, respective hidden layer, and respective output layers; and the estimating modules is further configured to determine the estimated value of the target feature based at least in part on an output of the hidden layer of the second neural network and to adjust the determined estimated value of the target feature based at least in part on an output of the hidden layer of the first neural network. 6. A system as claim 1 recites, wherein the input sets of the computational model correspond to respective ones of a plurality of time periods and the data domains include domains of one or more of the following per time period: completion counts, event counts, number of search queries, proportion of search queries, selections of search results, newly-created documents, newly-created documents in selected content repositories, social-media posts dates, date events, web-page visits. 7. A system as claim 6 recites, wherein a selected first one of the input sets of the computational model and a selected second one of the input sets of the computational model are separated along the temporal sequence by one day, one week, one month, or one year. 8. A system as claim 1 recites, wherein: the computational model includes respective neural networks for each of the input sets, each of the neural networks having an input layer, a hidden layer, and an output layer; the input-specific parameters correspond to neuron parameters of the input layer of the corresponding neural network; the history parameters correspond to neuron parameters of the hidden layer of the corresponding neural network; and the output layer of at least one of the neural networks is connected to the respective hidden layer(s) of one or more other(s) of the neural networks. 9. A system as claim 8 recites, wherein the respective output layer of each of the neural networks is connected to the respective hidden layer. 10. A system as claim 8 recites, wherein the input sets of the computational model correspond to a temporal sequence and a first one of the neural networks is connected to the respective hidden layer of one of the neural networks corresponding to an input set earlier in the temporal sequence than the input set of first one of the neural networks. 11. A system as claim 1 recites, wherein the extraction module is further configured to determine the values of at least one of the plurality of features based at least in part on records in at least one of the plurality of datasets including predetermined search keys. 12. A system as claim 1 recites, wherein the extraction module is further configured to: determine semantic features of records in at least one of the plurality of datasets; and determine the values of at least one of the plurality of features based at least in part on ones of the records having the semantic features corresponding to predetermined target semantic features. 13. A method, comprising: receiving, from a plurality of data sources, respective datasets organized according to respective unique data domains, wherein each dataset includes data value(s) associated with individual ones of a plurality of times; determining, using a processing unit, feature value(s) associated with each of the plurality of times based at least in part on the data value(s) associated with the corresponding ones of the plurality of times; and operating respective neural networks for each of the plurality of times to provide transformed feature value(s) based at least in part on ones of the feature value(s) associated with the corresponding ones of the plurality of times, wherein: a first one of the neural networks includes a hidden layer configured to provide feedforward value(s); a second, different one of the neural networks is configured to provide at least some of the respective transformed feature value(s) based at least in part on the feedforward value(s); and the time of the first one of the neural networks is earlier than the time of the second one of the neural networks. 14. A method as claim 13 recites, further comprising: receiving training value(s) associated with individual ones of the plurality of times; and modifying parameter(s) of individual ones of the neural networks based at least in part on at least some of the training value(s), feature value(s), and transformed feature value(s) associated with the corresponding ones of the plurality of times. 15. A method as claim 14 recites, wherein the modifying further comprises: determining error signal(s) for a selected one of the neural networks based at least in part on at least some of the corresponding training value(s) and at least some of the corresponding transformed feature value(s); and adjusting, based at least in part on the determined error signal(s), the parameter(s) of selected one(s) of the neural networks corresponding to time(s) earlier than the time of the selected one of the neural networks. 16. A method as claim 15 recites, wherein the adjusting comprises adjusting the parameters of each of the neural networks corresponding to a respective time earlier than the time of the selected one of the neural networks. 17. A method as claim 13 recites, wherein each of the neural networks is configured to provide at least some of the respective transformed feature value(s) based at least in part on one or more of the feedforward value(s) from each of the neural networks having an earlier time. 18. A computer-readable medium having thereon computer-executable instructions, the computer-executable instructions upon execution configuring a computer to perform operations comprising: extracting feature values from a plurality of datasets organized according to respective, different data domains, each feature value corresponding to a time; operating a plurality of neural networks to provide an estimated value of a target feature based at least in part on the feature values, wherein each of the neural networks corresponds to a respective relative time period and includes a respective hidden layer communicatively connected with the hidden layer of another of the neural networks having a later relative time period; determining an error value of the estimated value of the target feature based at least in part on a corresponding training value; and training the plurality of neural networks based at least in part on the error value and the feature values having times in the corresponding relative time period, wherein the training includes adjusting parameters of the respective hidden layers of at least two of the neural networks. 19. A computer-readable medium as claim 18 recites, the operations further comprising: extracting second feature values from a second plurality of datasets organized according to the respective, different data domains, each second feature value corresponding to a time later than any time of the extracted feature values; and operating the trained plurality of neural networks to provide a second estimated value of the target feature based at least in part on the second feature values, wherein each neural network of the trained plurality of neural networks is operated with extracted second feature values having times in the corresponding relative time periods. 20. A computer-readable medium as claim 18 recites, the operations for operating each of the plurality of neural networks further including determining the estimated value based at least in part on output(s) of the respective hidden layer and adjusting the estimated value based at least in part on output(s) of the hidden layer of the another of the neural networks.