Abstract: Described are examples for generating a model for forecasting time series data. For a timeseries data set, one or more layers can be provided, where each layer in the one or more layers includes, for each timeseries data input in at least a portion of multiple timeseries data inputs, generating, for the timeseries data input, a short range output from a causal convolution process that is based on timeseries data inputs from the timeseries data set that are associated with timestamps within a threshold time before the timestamp of the timeseries data input, and generating, for the timeseries data input, a long range output from a transformer process based on the short range outputs from the causal convolution process for each timeseries data input from at least the portion of the multiple timeseries data inputs that are associated with timestamps before the timestamp of the timeseries data input.

Abstract: Example aspects include techniques for anomaly detection via sparse judgmental samples. These techniques may include generating, via lexical analysis, a plurality of tokens from a textual representation of a machine learning (ML) model and generating, via a parser, based on the plurality of tokens, an abstract syntax tree (AST) corresponding to the ML model. In addition, the techniques may include identifying a data dependency of the ML model based on an AST node within the AST, the AST node corresponding to a data source and the data dependency indicating the ML model depends on the data source. Further, the techniques may include detecting a potential issue associated with the data source, and transmitting, based on the data dependency, an alert notification in response to the potential issue.

Abstract: A system can implement, in a first hyperparameter configuration state, a first set of hyperparameter search operations. The first set of hyperparameter search operations includes selecting a first set of hyperparameters. Each hyperparameter of the first set of hyperparameters having a corresponding configuration. Additionally, the first set of hyperparameter search operations includes obtaining a first set of performance data that includes information indicating a performance of each hyperparameter of the first set of hyperparameters, and assigning a value to each hyperparameter of the first set of hyperparameters based on the corresponding performance data.

Abstract: This application relates to apparatus and methods for optimizing hyperparameters for machine learning models. In some examples, a computing device configures a machine learning model with a first set of hyperparameters from a pool of hyperparameters. The computing device may execute the machine learning model to generate a validation score, and may update parameters of a probability determination model based on the validation score. Further, the computing device may execute the probability determination model to generate probabilities corresponding to the first set of hyperparameters. The computing device may also determine a second set of hyperparameters from the pool of hyperparameters based on the generated probabilities, and may configure the machine learning model with the second set of hyperparameters.

Abstract: Systems and methods for loyalty estimation are disclosed. A set of assortments each including a subset of a plurality of items is received and a loyalty score is calculated for each item in the plurality of items. The loyalty value is calculated based on a relationship between assortments including the item and assortments without the item.

Abstract: This application relates to apparatus and methods for optimizing hyperparameters for machine learning models. In some examples, a computing device configures a machine learning model with a first set of hyperparameters from a pool of hyperparameters. The computing device may execute the machine learning model to generate a validation score, and may update parameters of a probability determination model based on the validation score. Further, the computing device may execute the probability determination model to generate probabilities corresponding to the first set of hyperparameters. The computing device may also determine a second set of hyperparameters from the pool of hyperparameters based on the generated probabilities, and may configure the machine learning model with the second set of hyperparameters.