Abstract: Personally-stylized content can be generated without fine tuning a model. A personally-stylized content generation method can include receiving a first request for first content to be stylized in a style of written prose previously produced by a user, applying a previously trained retriever model to the first request to obtain second content previously produced by the user resulting in obtained content, populating a prompt with the obtained content and the first request resulting in an augmented prompt, providing the augmented prompt to a large language model (LLM), receiving personally-stylized content from the LLM, the personally-stylized content including elements of the style of the written prose of the user, and providing the personally-stylized content to the user.

Abstract: The present disclosure provides systems and methods for detecting and correlating anomalous time-series data. A system may receive and process time-series data associated with one or more network data streams to generate sets of aligned time-series data. The system may detect anomalous time-stamped data points in the sets of aligned time series data and generate groups of annotated time-series data. The annotation identifies specific time-stamped data points as anomalous. The system may determine the number of anomalous groups of annotated time-series data within all groups of annotated time-series data and may further determine the probability that one or more anomalous groups belong to at least one of the groups of annotated time-series data using a generative statistical model and outputting one or more correlated anomalous groups. The system may generate a detailed statistical report for each correlated anomalous group and output an aggregated statistical report for the correlated groups.

Abstract: The present disclosure provides systems and methods for detecting and correlating anomalous time-series data. A system may receive and process time-series data associated with one or more network data streams to generate sets of aligned time-series data. The system may detect anomalous time-stamped data points in the sets of aligned time series data and generate groups of annotated time-series data. The annotation identifies specific time-stamped data points as anomalous. The system may determine the number of anomalous groups of annotated time-series data within all groups of annotated time-series data and may further determine the probability that one or more anomalous groups belong to at least one of the groups of annotated time-series data using a generative statistical model and outputting one or more correlated anomalous groups. The system may generate a detailed statistical report for each correlated anomalous group and output an aggregated statistical report for the correlated groups.

Abstract: Various aspects of the subject technology related to systems and methods for detecting and correlating anomalous time-series data. A system may be configured to receive and process time-series data associated with one or more network data streams to generate sets of aligned time-series data. The system may detect anomalous time-stamped data points in the sets of aligned time series data and generate groups of annotated time-series data. The annotation identifies specific time-stamped data points as anomalous. The system may determine the number of anomalous groups of annotated time-series data within all groups of annotated time-series data and may further determine the probability that one or more anomalous groups belong to at least one of the groups of annotated time-series data using a generative statistical model and outputting one or more correlated anomalous groups.

Abstract: Various aspects of the subject technology related to systems and methods for detecting and correlating anomalous time-series data. A system may be configured to receive and process time-series data associated with one or more network data streams to generate sets of aligned time-series data. The system may detect anomalous time-stamped data points in the sets of aligned time series data and generate groups of annotated time-series data. The annotation identifies specific time-stamped data points as anomalous. The system may determine the number of anomalous groups of annotated time-series data within all groups of annotated time-series data and may further determine the probability that one or more anomalous groups belong to at least one of the groups of annotated time-series data using a generative statistical model and outputting one or more correlated anomalous groups.