Modularization

This release splits up the single java-timeseries package into two separate modules, math and timeseries. In addition, the arima package has undergone a major refactoring. Other notable changes.

• ARIMA models with drift. Drift terms are now available for modeling and forecasting. The semantics of the mean and drift terms align with those of the R forecast package.
• Time series linear regression models. Time series linear regression models may be created with relative ease. One feeds in a time series as a response variable and specifies whether to include an intercept, trend, and/or seasonal factors. Exogenous variables may also be added manually.
• Removal of unconditional-sum-of-squares (USS) fitting strategy. The strategies for fitting ARIMA models now coincide exactly with those in R. The default strategy is to use conditional-sum-of-squares (CSS) followed by maximum-likelihood (ML).