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: Systems and techniques are disclosed for machine learning based improvements in estimation techniques. An example method includes obtaining a seed specifying a portion of an action for evaluation with respect to a networked computing environment. A description of the action is completed by providing a first recurrent neural network (RNN) with the seed to generate one or more additional words, with the action represented by the description not being enabled yet in the networked computing environment. An estimated down-stream impact (DSI) associated with the action is determined based on a second RNN, with the estimated DSI indicating an estimated measure of impact to the networked computing environment which would be caused by performance of the example action after enabling the example action. Output of an interactive user interface including information representing the action and the estimated DSI is caused.

Abstract: A system is provided for analyzing item description attributes and user profile attributes, and dynamically generating item description content based on the analysis. The system can obtain item descriptions for a particular item from any number of different sources and/or in any number of different formats. The system can process the item descriptions to determine item attributes. The system can also obtain data regarding user profile attributes. The system may dynamically generate item description content for the particular item and user based on analyzing the item attributes and user profile attributes. The item description content may be further tailored to a particular channel of delivery.

Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for automatic execution of actions responsive to queries. Example methods include determining a set of seed words for input at a first search engine, determining a first autocomplete result associated with a first word at the first search engine, and determining a second autocomplete result associated with the first autocomplete result at the first search engine. Example methods include determining a set of user interaction histories that comprises queries input at a second search engine, and determining that a first user interaction history includes a query with a portion of the second autocomplete result. Example methods include determining a user action performed after the query, and associating the user action with the query in a database, such that subsequent inputs of the query cause the first user action to automatically occur.