Abstract: Aspects of the disclosure are directed to an approach for training a multivariate time series forecasting model using linear regression and ARIMA. The training may be performed by accessing data stored in a data warehouse using structure query language commands. The disclosure further provides for forecasting utilizing the trained model.

Abstract: A technology for retrieving data from a database. The technology includes receiving a search query specifying a target attribute and a target attribute value, accessing an index to determine one or more target files in which the target attribute value appears, the index including a plurality of attribute values, and for each of the attribute values, one or more files in which the attribute value appears, and retrieving data from the one or more target files.

Abstract: A technology for retrieving data from a database. The technology includes receiving a search query specifying a target attribute and a target attribute value, accessing an index to determine one or more target files in which the target attribute value appears, the index including a plurality of attribute values, and for each of the attribute values, one or more files in which the attribute value appears, and retrieving data from the one or more target files.

Abstract: A method for forecasting time-series data, when executed by data processing hardware, causes the data processing hardware to perform operations including receiving a time series forecasting query from a user requesting a time series forecast forecasting future data based on a set of current time-series data. The operations include obtaining, from the set of current time-series data, a set of training data. The operations include training, using a first portion of the set of training data, a first sub-model of a forecasting model and training, using a second portion of the set of training data, a second sub-model of the forecasting model. The second portion is different than the first portion. The operations include forecasting, using the forecasting model, the future data based on the set of current time-series data and returning, to the user, the forecasted future data for the time series forecast.

Abstract: A technology for retrieving data from a database. The technology includes receiving a search query specifying a target attribute and a target attribute value, accessing an index to determine one or more target files in which the target attribute value appears, the index including a plurality of attribute values, and for each of the attribute values, one or more files in which the attribute value appears, and retrieving data from the one or more target files.

Abstract: A method for principal component analysis includes receiving a principal component analysis (PCA) request from a user requesting data processing hardware to perform PCA on a dataset, the dataset including a plurality of input features. The method further includes training a PCA model on the plurality of input features of the dataset. The method includes determining, using the trained PCA model, one or more principal components of the dataset. The method also includes generating, based on the plurality of input features and the one or more principal components, one or more embedded features of the dataset. The method includes returning the one or more embedded features to the user.