Quantum Feature Selection for ML

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...

Long-Memory Dynamic Factor Model (LMDFM)

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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 ...

Feature Selection for Machine Learning

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...

Bayesian filtering Factor Analysis VBfFA

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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 time-series....

Long-Memory Vector Autoregression, LMVAR

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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...

Mphasis HyperGraf Topic Modeling

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...

Dynamic Factor Variance-Cov Model, DFVCM

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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 com...

Yule-Walker-PCA Autoregression (YWpcAR)

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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"....

DFM Forecast of Volatility Index, DFbVIF

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DFbVIF (dynamic factor based volatility index forecast) model/algorithm is to make multi-step forecasts of multiple volatility indexes. Widely watched volatility indexes include VIX (on S&P 500) and VXN (on Nasdaq 100) published by CBOE. Holistic data-driven models analyzing and forecasting...