Dynamically updating a ridge regression data model of a continuous stream of data is disclosed. New data chunks corresponding to a current data accumulation point are received and the data values in the new data chunks are transformed via standardization methods. A ridge estimator for the standardized data that includes data chunks received up to a penultimate data accumulation point to include the new data chunks is dynamically updated. The cumulative observations received up to the current data accumulation point are updated and stored. Predictions for the continuous data stream are generated based on the updated ridge estimator.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus comprising: a processor; a non-transitory data storage medium comprising processor-readable instructions that when executed cause the processor to: receive a data stream from a data source, the data stream comprising data values corresponding to a series of data accumulation points; determine, for a subset of the data accumulation points, values of parameters including an optimal shrinkage parameter of a ridge regression data model representing the data stream wherein the subset of data accumulation points includes a penultimate data accumulation point; cache data values received subsequent to the penultimate data accumulation point; estimate values of the parameters for a current data accumulation point immediately following the subset of data accumulation points based on stored values of the parameters and the cached data values; update the data model with the values of the parameters for the current data accumulation point; and provide, via a user interface, predictions regarding the data stream, the predictions based on the updated data model. 2. The apparatus of claim 1, wherein the data stream is received from a population having a substantially similar distribution. 3. The apparatus of claim 1, wherein to determine the values of the parameters the processor is to: generate standardized data by standardizing the data values received in the data stream. 4. The apparatus of claim 3, wherein to determine the values of the parameters, the processor is to: calculate a value of a ridge estimator for the standardized data. 5. The apparatus of claim 1, wherein the current data accumulation point is a consecutive data accumulation point that is subsequent to the penultimate data accumulation point. 6. The apparatus of claim 5, wherein to cache the data values received subsequent to the penultimate data accumulation point, the processor is to: cache the data values received during a time interval subsequent to the penultimate data accumulation point and preceding the current data accumulation point. 7. The apparatus of claim 1, the processor is to further: determine if further data chunks comprising new data values are to be received in the data stream. 8. The apparatus of claim 7, the processor is to further: repeat the steps of caching the data values, estimating values of the parameters for the current data accumulation point, updating the data model and providing the predictions based on the updated data model for each new data chunk received in the data stream. 9. The apparatus of claim 1, wherein to provide predictions regarding the data stream, the processor is to: update information provided to a user on the user interface, the information based on the updated data model. 10. A method of providing predictions, comprising: receiving, by a computing apparatus, new data chunks in a data stream during a time period between a current data accumulation point and a penultimate data accumulation point; transforming, by the computing apparatus, data values in the new data chunks via standardization methods; dynamically updating, by the computing apparatus, a data model of the data stream to include the new data chunks, the data model being updated via estimating a ridge estimator for standardized data that includes data chunks received up to the penultimate data accumulation point; updating, by the computing apparatus, cumulative observations up to the current data accumulation point; and generating, by the computing apparatus, predictions for the data stream based on the updated data model. 11. The method of claim 10, further comprising: determining, by the computing apparatus, the ridge estimator for the standardized data including the data chunks received up to the penultimate data accumulation point. 12. The method of claim 11, further comprising: obtaining, by the computing apparatus, an optimal shrinkage parameter using cross-validation. 13. A non-transitory computer readable data storage medium storing processor-readable instructions executable by at least one processor to: receive new data chunks in a data stream during a time period between a current data accumulation point and a penultimate data accumulation point; transform data values received in the new data chunks via standardization methods; update a ridge regression data model of the data stream to include the new data chunks via estimating the data model for the transformed data values, the ridge regression data model includes data chunks received up to the penultimate data accumulation point; update cumulative observations up to the current data accumulation point; and generate predictions for the data stream based on the updated data model. 14. The non-transitory computer-readable data storage medium of claim 13, wherein data in the data stream has a same underlying distribution. 15. The non-transitory computer-readable data storage medium of claim 13, further comprising instructions that cause the processor to: update a user interface based on the predictions generated from the updated data model.