Abstract: Systems and methods for scoring investment data using machine learning-based model training. The method includes receiving historical data over a time period. The method further includes determining positive investment data and negative investment data based on the historical data and investment preference data. The positive investment data including characteristics associated with positive assets that align with the investment preference data. The negative investment data including characteristics associated with negative assets that misalign with the investment data. The method further includes calculating machine learning model parameters based on the positive and negative investment data. The method also includes calculating a score corresponding to a new asset based on the machine learning model parameters and new investment data. The method further includes determining whether the new investment data aligns with the investment preference data based on the score and a threshold investment score.

Abstract: Systems and methods for scoring investment data using machine learning-based model training. The method includes receiving historical data over a time period. The method further includes determining positive investment data and negative investment data based on the historical data and investment preference data. The positive investment data including characteristics associated with positive assets that align with the investment preference data. The negative investment data including characteristics associated with negative assets that misalign with the investment data. The method further includes calculating machine learning model parameters based on the positive and negative investment data. The method also includes calculating a score corresponding to a new asset based on the machine learning model parameters and new investment data. The method further includes determining whether the new investment data aligns with the investment preference data based on the score and a threshold investment score.

Abstract: A method for determining a customer service metric using time-based predictive association based on user interactions over interaction channels. Events associated with intended transaction types are identified as characterized or uncharacterized, and sorted chronologically based on when the event occurred. Sequences of events are created based on analysis of each event against a chronologically consecutive later event and a determination of whether each event is uncharacterized and occurred before the later event and the sequence does not include a characterized event, or, that the earlier event is a characterized event or the sequence includes a characterized event, and the earlier event occurred a period of time before the later event. The later event is appended to a sequence, or an additional sequence is created including the later event, and the sequences are filtered. A customer service metric is determined based on a duration of time calculated for each sequence.

Abstract: A method for use in calculating a possible power output of a wind turbine. A series of performance data samples is acquired. Each performance data sample includes a meteorological condition and a power output indicated at a first time by one or more sensors associated with a wind turbine. A transfer function is calculated based at least in part on the series of performance data samples. The transfer function relates power output to the meteorological condition. A possible power output is calculated based on the transfer function and at least one meteorological condition indicated by the one or more sensors at a second time.

Abstract: A method for use in calculating a possible power output of a wind turbine. A series of performance data samples is acquired. Each performance data sample includes a meteorological condition and a power output indicated at a first time by one or more sensors associated with a wind turbine. A transfer function is calculated based at least in part on the series of performance data samples. The transfer function relates power output to the meteorological condition.