Abstract: A foundation model is trained on time series-related data. The foundation model is configured to take as input time series data (e.g., asset prices in a market, power demand in a power grid, scores in baseball games, etc.) as well as related time-stamped exogenous data having a different modality from the time-series data (e.g., news headlines). Once the foundation model is trained, it may be fine-tuned for different decoder heads to make predictions for a range of time series values.

Abstract: Computer image clustering systems and methods for conducting effective media searches by grouping multimedia documents tagged by keywords into a hierarchy of images configured to: (1) maintain a first database, (2) maintain an initial occurrence matrix, (3) maintain an occurrence matrix, (4) maintain a media file activation score for each media file in the first database, (5) generate a log version of the occurrence matrix, (6) maintain an inverse media file frequency value for each descriptive term in the first database, (7) generate a descriptive term frequency matrix and generate a list of document vectors in multidimensional space (list), and (8) organize and process each media file in the list into a high activation score category and a low activation score category.

Abstract: Computer image clustering systems and methods for conducting effective media searches by grouping multimedia documents tagged by keywords into a hierarchy of images configured to: (1) maintain a first database, (2) maintain an initial occurrence matrix, (3) maintain an occurrence matrix, (4) maintain a media file activation score for each media file in the first database, (5) generate a log version of the occurrence matrix, (6) maintain an inverse media file frequency value for each descriptive term in the first database, (7) generate a descriptive term frequency matrix and generate a list of document vectors in multidimensional space (list), and (8) organize and process each media file in the list into a high activation score category and a low activation score category.

Abstract: Computer image clustering systems and methods for conducting effective media searches by grouping multimedia documents tagged by keywords into a hierarchy of images configured to: (1) maintain a first database, (2) maintain an initial occurrence matrix, (3) maintain an occurrence matrix, (4) maintain a media file activation score for each media file in the first database, (5) generate a log version of the occurrence matrix, (6) maintain an inverse media file frequency value for each descriptive term in the first database, (7) generate a descriptive term frequency matrix and generate a list of document vectors in multidimensional space (list), and (8) organize and process each media file in the list into a high activation score category and a low activation score category.