seasonal and non seasonal undifferencing

season_nonseasonal_undifferencing.py

def undiff(data, seasonal, nonseasonal, xi):
    
    print("you have to know what xi for which use case you are going to use")
    
    #nonseasonal
    if(nonseasonal!=0 and seasonal==0):
        temp = np.concatenate([np.array(xi),np.array(data)])
        temp_ = diff_inv(temp,1,nonseasonal)
        return(temp_[-len(data):])
        
    #seasonal
    if(seasonal!=0 and nonseasonal == 0):
        temp = np.concatenate([np.array(xi),np.array(data)])
        temp_ = diff_inv(temp,season,1)
        return(temp_[-len(data):])
    
    #both
    if(seasonal==1 and nonseasonal == 1):

        temp_ = np.concatenate([np.array(xi),np.array(data)])
        
        initial_seasonal_delta = xi.iloc[season]-xi.iloc[0]
        s_diffed_cat = np.concatenate([[np.array(initial_seasonal_delta)],np.array(data.dropna())])

        ns_undiffed = diff_inv(s_diffed_cat,1,1)[-len(temp_):]

        ns_diffed_cat = np.concatenate([np.array(xi[1:(season+1)]),np.array(ns_undiffed)])
        s_undiffed = diff_inv(ns_diffed_cat,season,1)[-len(ns_undiffed):]

        return(s_undiffed[-len(data):])
        
def difference(data, seasonal, nonseasonal):

    if seasonal > 0:
        return(data.diff(periods=season).diff(nonseasonal))
    elif season > 0:
        return(data.diff(nonseasonal))
    else:
        return(data)
    
#non seasonal
#undiff(temp_test['target'].dropna(), 0, nonseasonal,[raw_int[chosen].loc[temp_train['target'].index[-1]]])
#test
#undiff(temp_test[chosen],seasonal,nonseasonal,raw_int[chosen].loc[temp_train['target'].index[-1:]])
#train
#train_prior_date = raw_int[chosen].index[np.argwhere(data_final.index==temp_train['target'].index[0]).ravel()[0]-nonseasonal]
#train_xi=[raw_int[chosen].loc[train_prior_date]]
#undiff(temp_train['target'], seasonal, nonseasonal,xi)

#seasonal
#undiff(raw_int[chosen].diff(periods=4).dropna(),1,0,raw_int[chosen][0:season])

#non seasonal and seasonal
#undiff(raw_int[chosen].diff(periods=4).diff().dropna(),seasonal,nonseasonal,raw_int[chosen][0:season+nonseasonal])

diff_inv([*np.log(weighted_portfolio).values[0:2],*np.log(weighted_portfolio).diff(2).dropna().values],1,2)