63 lines
2.5 KiB
Python
63 lines
2.5 KiB
Python
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import logging
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import os
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from neuralforecast import NeuralForecast
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from neuralforecast.models import NHITS
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from neuralforecast.utils import AirPassengersPanel
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from mlforecast.utils import PredictionIntervals
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from neuralforecast.losses.pytorch import DistributionLoss, MAE
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os.environ['NIXTLA_ID_AS_COL'] = '1'
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AirPassengersPanel_train = AirPassengersPanel[AirPassengersPanel['ds'] < AirPassengersPanel['ds'].values[-12]].reset_index(drop=True)
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AirPassengersPanel_test = AirPassengersPanel[AirPassengersPanel['ds'] >= AirPassengersPanel['ds'].values[-12]].reset_index(drop=True)
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AirPassengersPanel_test['y'] = np.nan
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AirPassengersPanel_test['y_[lag12]'] = np.nan
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horizon = 12
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input_size = 24
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prediction_intervals = PredictionIntervals()
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models = [NHITS(h=horizon, input_size=input_size, max_steps=100, loss=MAE(), scaler_type="robust"),
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NHITS(h=horizon, input_size=input_size, max_steps=100, loss=DistributionLoss("Normal", level=[90]), scaler_type="robust")]
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nf = NeuralForecast(models=models, freq='ME')
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nf.fit(AirPassengersPanel_train, prediction_intervals=prediction_intervals)
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preds = nf.predict(futr_df=AirPassengersPanel_test, level=[90])
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize = (20, 7))
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plot_df = pd.concat([AirPassengersPanel_train, preds])
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plot_df = plot_df[plot_df['unique_id']=='Airline1'].drop(['unique_id','trend','y_[lag12]'], axis=1).iloc[-50:]
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ax1.plot(plot_df['ds'], plot_df['y'], c='black', label='True')
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ax1.plot(plot_df['ds'], plot_df['NHITS'], c='blue', label='median')
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ax1.fill_between(x=plot_df['ds'][-12:],
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y1=plot_df['NHITS-lo-90'][-12:].values,
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y2=plot_df['NHITS-hi-90'][-12:].values,
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alpha=0.4, label='level 90')
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ax1.set_title('AirPassengers Forecast - Uncertainty quantification using Conformal Prediction', fontsize=18)
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ax1.set_ylabel('Monthly Passengers', fontsize=15)
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ax1.set_xticklabels([])
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ax1.legend(prop={'size': 10})
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ax1.grid()
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ax2.plot(plot_df['ds'], plot_df['y'], c='black', label='True')
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ax2.plot(plot_df['ds'], plot_df['NHITS1'], c='blue', label='median')
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ax2.fill_between(x=plot_df['ds'][-12:],
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y1=plot_df['NHITS1-lo-90'][-12:].values,
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y2=plot_df['NHITS1-hi-90'][-12:].values,
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alpha=0.4, label='level 90')
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ax2.set_title('AirPassengers Forecast - Uncertainty quantification using Normal distribution', fontsize=18)
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ax2.set_ylabel('Monthly Passengers', fontsize=15)
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ax2.set_xlabel('Timestamp [t]', fontsize=15)
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ax2.legend(prop={'size': 10})
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ax2.grid()
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