Abstract: A method and system for generating keywords that distinguish a group of selected documents from a larger corpus. The method includes: receiving a corpus that includes a plurality of documents; selecting from the corpus, based on at least one predetermined criterion, a group of the documents; determining a first Inverse Document Frequency (IDF) score for each word in a vocabulary of the group with respect to the corpus; determining a second IDF score for each word in the vocabulary of the group with respect to the group; generating based on the first IDF score and the second IDF score, a Group Frequency Inverse Document Frequency (GFIDF) score for each word in the vocabulary of the group; and assigning based on the generated GFIDF score for each word in the vocabulary of the group, at least one keyword.

Abstract: A method and a system for detecting an anomalous sequence of events in stream data are provided. The method includes: receiving a first set of raw data; analyzing the first set of raw data in order to determine a first event sequence; applying a first Hidden Markov Model (HMM) to the first event sequence in order to generate a first output; and determining, based on the first output, whether the first event sequence is classifiable as being an anomalous event sequence. The HMM is trained by using known sequences of normal events and event sequences that are known to be anomalous.

Abstract: Methods and systems for using images that represent time-series data to forecast corresponding images depicting future values of the time-series data are provided. The method includes: receiving a set of time-series data; converting the set of time-series data into a partial first image that includes a blank region to which future data to be included in the first set of time-series data corresponds; and performing an inpainting operation with respect to the partial first image by generating pixels for filling in the blank region in order to produce an augmented version of the first image. A machine learning algorithm that is trained by using historical time-series data may be used to perform the inpainting operation.

Abstract: A method for using images that represent time-series data to forecast future images depicting future values as pixelated information is provided. The method includes: receiving a first set of time-series data; converting the received first set of time-series data into a first image; and using the first image to forecast a future image depicting future values as pixelated information that corresponds to a future time interval Training sets of time-series data are used to generate historical data that provides input to a machine learning algorithm, which provides, as an output, a composite image that depicts the future values as pixelated information that reflects associated uncertainties in the value predictions.