*All numbers represent the number of sessions for each node in the Hierarchy

Parameters
----------------
 y - dataframe of time-series data
           Layout:
               0th Col - Time instances
               1st Col - Total of TS
               2nd Col - One of the children of the Total TS
               3rd Col - The other child of the Total TS
               ...
               ... Rest of the 1st layer
               ...
               Xth Col - First Child of the 2nd Col
               ...
               ... All of the 2nd Col's Children
               ...
               X+Yth Col - First Child of the 3rd Col
               ...
               ..
               .   And so on...

 h - number of step ahead forecasts to make (int)

 nodes - a list or list of lists of the number of child nodes at each level
 Ex. if the hierarchy is one total with two child nodes that comprise it, the nodes input would be [2]
 
 method – (String)  the type of hierarchical forecasting method that the user wants to use. 
            Options:
            "OLS" - optimal combination using ordinary least squares (Default), 
            "WLSS" - optimal combination using structurally weighted least squares, 
            "WLSV" - optimal combination using variance weighted least squares, 
            "FP" - forcasted proportions (top-down)
            "PHA" - proportions of historical averages (top-down)
            "AHP" - average historical proportions (top-down)
            "BU" - bottom-up (simple addition)
            "CVselect" - select which method is best for you based on 3-fold Cross validation (longer run time)
 
 freq - (Time Frequency) input for the forecasting function of Prophet 
 
 include_history - (Boolean) input for the forecasting function of Prophet
 
 transform - (None or "BoxCox") Do you want to transform your data before fitting the prophet function? If yes, type "BoxCox"
            
 cap - (Dataframe or Constant) carrying capacity of the input time series.  If it is a dataframe, then
                               the number of columns must equal len(y.columns) - 1
                               
 capF - (Dataframe or Constant) carrying capacity of the future time series.  If it is a dataframe, then
                                the number of columns must equal len(y.columns) - 1
 
 changepoints - (DataFrame or List) changepoints for the model to consider fitting. If it is a dataframe, then
                                    the number of columns must equal len(y.columns) - 1
 
 n_changepoints - (constant or list) changepoints for the model to consider fitting. If it is a list, then
                                     the number of items must equal len(y.columns) - 1
 skipFitting - (Boolean) if y is already a dictionary of dataframes, set this to True, and DO NOT run with method = "cvSelect" or transform = "BoxCox"
 
 numThreads - (int) number of threads you want to use when running cvSelect. Note: 14 has shown to decrease runtime by 10 percent 
 
 All other inputs - see Prophet
 
Returns
-----------------
 ynew - a dictionary of DataFrames with predictions, seasonalities and trends that can all be plotted