1. A method for predictive modeling, the method comprising: generating, via a computer processing device, a factor matrix for each univariate time series data in a set of sparse time series data collected from data sources operating in a railroad environment, the time series data including at least one of temperature, optical geometry, load, and acoustic data; identifying a subset of the time series data as a feature selection based on application of a loss function; generating a predictive model from the subset of the time series data; and generating, from the predictive model, a prediction indicating an amount of time before a failure occurs with respect to a component of the railroad environment.