Neural/MLForecast

!pip install datasetsforecast hierarchicalforecast mlforecast neuralforecast

import numpy as np
import pandas as pd

from datasetsforecast.hierarchical import HierarchicalData

from neuralforecast import NeuralForecast
from neuralforecast.models import NBEATS
from neuralforecast.losses.pytorch import GMM

from mlforecast import MLForecast
from mlforecast.utils import PredictionIntervals
import xgboost as xgb

#obtain hierarchical reconciliation methods and evaluation
from hierarchicalforecast.methods import BottomUp, ERM, MinTrace
from hierarchicalforecast.utils import HierarchicalPlot
from hierarchicalforecast.core import HierarchicalReconciliation
from hierarchicalforecast.evaluation import evaluate

Y_df, S_df, tags = HierarchicalData.load('./data', 'TourismLarge')
Y_df['ds'] = pd.to_datetime(Y_df['ds'])
S_df = S_df.reset_index(names="unique_id")

Y_df.head()

hplot = HierarchicalPlot(S=S_df, tags=tags)
hplot.plot_summing_matrix()

horizon = 12
Y_test_df = Y_df.groupby('unique_id', as_index=False).tail(horizon)
Y_train_df = Y_df.drop(Y_test_df.index)

level = np.arange(0, 100, 2)
qs = [[50-lv/2, 50+lv/2] for lv in level]
quantiles = np.sort(np.concatenate(qs)[1:]/100)

#fit/predict NBEATS from NeuralForecast
nbeats = NBEATS(h=horizon,
              input_size=2*horizon,
              loss=GMM(n_components=10, quantiles=quantiles),
              scaler_type='robust',
              max_steps=2000)
nf = NeuralForecast(models=[nbeats], freq='MS')
nf.fit(df=Y_train_df)
Y_hat_nf = nf.predict()
insample_nf = nf.predict_insample(step_size=horizon)

#fit/predict XGBRegressor from MLForecast
mf = MLForecast(models=[xgb.XGBRegressor()], 
                freq='MS',
                lags=[1,2,12,24],
                date_features=['month'],
                )
mf.fit(Y_train_df, fitted=True, prediction_intervals=PredictionIntervals(n_windows=10, h=horizon)) 
Y_hat_mf = mf.predict(horizon, level=level)
insample_mf = mf.forecast_fitted_values()

Y_hat_nf

Y_hat_mf

reconcilers = [
    ERM(method='closed'),
    BottomUp(),
    MinTrace('mint_shrink'),
]
hrec = HierarchicalReconciliation(reconcilers=reconcilers)

Y_rec_nf = hrec.reconcile(Y_hat_df=Y_hat_nf, Y_df=insample_nf, S=S_df, tags=tags, level=level)
Y_rec_mf = hrec.reconcile(Y_hat_df=Y_hat_mf, Y_df=insample_mf, S=S_df, tags=tags, level=level)

from utilsforecast.losses import scaled_crps

rec_model_names_nf = ['NBEATS/BottomUp', 'NBEATS/MinTrace_method-mint_shrink', 'NBEATS/ERM_method-closed_lambda_reg-0.01']

evaluation_nf = evaluate(df = Y_rec_nf.merge(Y_test_df, on=['unique_id', 'ds']),
                      tags = tags,
                      metrics = [scaled_crps],
                      models= rec_model_names_nf,
                      level = list(range(0, 100, 2)),
                      )

rec_model_names_mf = ['XGBRegressor/BottomUp', 'XGBRegressor/MinTrace_method-mint_shrink', 'XGBRegressor/ERM_method-closed_lambda_reg-0.01']

evaluation_mf = evaluate(df = Y_rec_mf.merge(Y_test_df, on=['unique_id', 'ds']),
                      tags = tags,
                      metrics = [scaled_crps],
                      models= rec_model_names_mf,
                      level = list(range(0, 100, 2)),
                      )

name = 'NBEATS/BottomUp'
quantile_columns = [col for col in Y_rec_mf.columns if (name+'-lo') in col or (name+'-hi') in col]

evaluation_nf.query("level == 'Overall'")

evaluation_mf.query("level == 'Overall'")

plot_nf = Y_df.merge(Y_rec_nf, on=['unique_id', 'ds'], how="outer")

plot_mf = Y_df.merge(Y_rec_mf, on=['unique_id', 'ds'], how="outer")

hplot.plot_series(
    series='TotalVis',
    Y_df=plot_nf, 
    models=['y', 'NBEATS', 'NBEATS/BottomUp', 'NBEATS/MinTrace_method-mint_shrink', 'NBEATS/ERM_method-closed_lambda_reg-0.01'],
    level=[80]
)

hplot.plot_series(
    series='TotalVis',
    Y_df=plot_mf, 
    models=['y', 'XGBRegressor', 'XGBRegressor/BottomUp', 'XGBRegressor/MinTrace_method-mint_shrink', 'XGBRegressor/ERM_method-closed_lambda_reg-0.01'],
    level=[80]
)