Neuralforecast

from neuralforecast import NeuralForecast

from neuralforecast.models import LSTM, NHITS, RNN

from neuralforecast.utils import AirPassengersDF

Y_df = AirPassengersDF

horizon = 12

models = [LSTM(h=horizon,                    # Forecast horizon

               max_steps=500,                # Number of steps to train

               scaler_type='standard',       # Type of scaler to normalize data

               encoder_hidden_size=64,       # Defines the size of the hidden state of the LSTM

               decoder_hidden_size=64,),     # Defines the number of hidden units of each layer of the MLP decoder

          NHITS(h=horizon,                   # Forecast horizon

                input_size=2 * horizon,      # Length of input sequence

                max_steps=100,               # Number of steps to train

                n_freq_downsample=[2, 1, 1]) # Downsampling factors for each stack output

          ]

nf = NeuralForecast(models=models, freq='M')

nf.fit(df=Y_df)

Y_hat_df = nf.predict()

Y_hat_df = Y_hat_df.reset_index()

import pandas as pd

import matplotlib.pyplot as plt

fig, ax = plt.subplots(1, 1, figsize = (20, 7))

plot_df = pd.concat([Y_df, Y_hat_df]).set_index('ds') # Concatenate the train and forecast dataframes

plot_df[['y', 'LSTM', 'NHITS']].plot(ax=ax, linewidth=2)

ax.set_title('AirPassengers Forecast', fontsize=22)

ax.set_ylabel('Monthly Passengers', fontsize=20)

ax.set_xlabel('Timestamp [t]', fontsize=20)

ax.legend(prop={'size': 15})

ax.grid()