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The Dynamic Factor Variance-Covariance Model (DFVCM) makes multi-step forecasts of multivariate volatilities of a large number time-series (e.g. those of numerous investable assets in many markets) by applying dynamic factor model (DFM). The multi-step forecasts of multivariate volatilities are...
Algorithm - Fulfilled on Amazon SageMaker
Quantum Feature Selecton is hyrbid quantum computing approach to optimize feature selection in artificial intelligence/machine learning (AI/ML) model training and prediction. This solution approaches feature selection as an optimization problem and selects the most critical variables and eliminates...
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The variational Bayesian filtering factor analysis (VBfFA) algorithm/model is a filter with dimension-reduction, or rank-reduction, to extract a number of ever-evolving unobserved common factors, or signals from common sources, underlying and influencing a large number of related...
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The solution runs machine learning related feature selection operations on the input data. This will simplify the task of feature selection for a data scientist where the user will have to specify few selected parameters to generate the correct output instead of writing specific code for each...
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The Yule-Walker-PCA Autoregressive Model (YWpcAR) algorithm is developed to simultaneously analyze and forecast many time-series individually, assuming each time-series is influenced by evolution of "hidden components" (resulted from PCA). Here PCA standards for "principal components analysis"....
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Topic Modeling solution clusters words/phrases into abstract topics. This solution helps in understanding document content, category and document similarity. Given a set of training documents, this module trains a model and maps the top five relevant abstract topics which have high content...
Algorithm - Fulfilled on Amazon SageMaker
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Singular Value Decomposition based dimensionality reduction on sparse data set like LibSVM without translating the data set into other formats like recordIO. The algorithm scales efficiently across multi-cores on a single AWS EC2 Instance out of the box.
Algorithm - Fulfilled on Amazon SageMaker
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The long-memory dynamic factor model (LMDFM) algorithm is to make (1) analysis of observed multiple (vector) time-series, (2) multi-step forecasts of multivariate (vector) time-series, and (3) multi-step forecasts of multivariate volatility (variance-covariance matrix) of vector time-series. The...
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The continuously trained vector autoregressive forecast (CTVARF) model is to forecast large set of time-series based always on the latest updated model trained by the latest available data. The time-series are assumed to be influenced by histories of a set of unobserved factors commonly affecting...
Algorithm - Fulfilled on Amazon SageMaker
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The long-memory vector autoregressive (LMVAR) model is to analyze and forecast large number of time-series when they are influenced by both (a) evolution histories of a set of unobserved factors commonly affecting all or many time-series and (b) histories of hidden components affecting...
Algorithm - Fulfilled on Amazon SageMaker
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