Abstract: Systems, articles, and computer-implemented methods are disclosed for generating natural language summaries of a multi-dimensional analysis of a detected anomaly within a member of multi-dimensional data by prompting a LLM with a prompt generated to include data about the anomaly in a manner understandable by the LLM. The prompt to the LLM includes a path to a member of the hierarchy containing an anomaly with a delimiter between the member and ancestor nodes. The delimiter allows the ancestral context of the member of the hierarchy to be understood by the LLM. The prompt also includes a metric defining a magnitude of the anomaly in relation to another value, such as an average, a value of the anomaly, a time corresponding to the anomaly, and one or more examples of other anomalies with included data about those anomalies matching the type of data provided for the detected anomaly.

Abstract: Various aspects of the present disclosure generally relate to universal flash storage (UFS) devices. In some aspects, a UFS controller may receive, from a UFS driver, a command associated with a storage transaction for an application that runs on a processor. The UFS controller may identify, from the command, a data transfer size associated with the command. The UFS controller may identify, from a register, a power management quality of service (PM-QoS) control data transfer size. The UFS controller may send, to the processor, a PM-QoS processor vote based at least in part on the data transfer size associated with the command in relation to the PM-QoS control data transfer size, wherein the PM-QoS processor vote prevents the processor from entering a low power state until the storage transaction is completed. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to universal flash storage (UFS) devices. In some aspects, a UFS controller may receive, from a UFS driver, a command associated with a storage transaction for an application that runs on a processor. The UFS controller may identify, from the command, a data transfer size associated with the command. The UFS controller may identify, from a register, a power management quality of service (PM-QoS) control data transfer size. The UFS controller may send, to the processor, a PM-QoS processor vote based at least in part on the data transfer size associated with the command in relation to the PM-QoS control data transfer size, wherein the PM-QoS processor vote prevents the processor from entering a low power state until the storage transaction is completed. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to memory devices. In some aspects, a host device may detect a background operation status associated with a memory device. The host device may transmit, to the memory device and based at least in part on the background operation status, a command to disable a background operation associated with the memory device. The host device may transmit, to the memory device, a power down command in accordance with a timeout associated with the background operation. The host device may receive, from the memory device, a response. The host device may instruct, based at least in part on the response, one or more resources associated with the memory device to enter into a low power mode state or an off state. Numerous other aspects are described.

Abstract: Systems, articles, and computer-implemented methods are provided for generating summaries of a plurality of insights in multi-dimensional data to describe underlying trends using a large language model. A data structure is generated describing the plurality of insights where the data structure encapsulates for each insight of the plurality of insights to be included: a member of a data hierarchy that fits a descendant dimension that includes the insight, a value of the descendant dimension that fits the insight, and a characteristic of the insight. The data structure is included within a prompt to a large language model to summarize the plurality of insights. The prompt may also include data representing a relationship between the plurality of insights, such as how a first insight of the plurality of insights contributes to a second insight of the plurality of insights.

Abstract: Systems, articles, and computer-implemented methods are disclosed for generating natural language summaries of a multi-dimensional analysis of a detected anomaly within a member of multi-dimensional data by prompting a LLM with a prompt generated to include data about the anomaly in a manner understandable by the LLM. The prompt to the LLM includes a path to a member of the hierarchy containing an anomaly with a delimiter between the member and ancestor nodes. The delimiter allows the ancestral context of the member of the hierarchy to be understood by the LLM. The prompt also includes a metric defining a magnitude of the anomaly in relation to another value, such as an average, a value of the anomaly, a time corresponding to the anomaly, and one or more examples of other anomalies with included data about those anomalies matching the type of data provided for the detected anomaly.

Abstract: The present embodiments relate to generating input parameters for selecting a forecasting model. An example method includes a computing device receiving a time series comprising a plurality of data points, wherein each data point of the time series comprises a time associated with the data point and a value. The device can identify a first season and a second season from the time series, wherein a length of the first season is a factor of a length of the second season. The device can estimate a Fourier order and a seasonality mode for the first season based at least in part on the length of the first season and the length of the second season. The device can select a forecasting model to forecast a value of a future time step of the time series based at least in part on the Fourier order and the seasonality mode.

Abstract: Techniques are described for providing explanation information for time series-based predictions made using statistical models, such as linear statistical models, examples of which include various Exponential Smoothing models, Autoregressive Integrated Moving Average (ARIMA) models, and others. For a forecast predicted by a statistical model that has been trained upon and/or fit to a set of historical times series data points, an explanation is generated for the forecast, where the explanation for the forecast includes information indicative of the importance or impact or influence of individual time series data points in the set on the forecast. The explanation for the forecast may be output to a user along with the forecast. This enables the user to have some visibility into why the particular forecast was predicted by the statistical model.