Spaces:
Sleeping
Sleeping
import pandas as pd | |
import matplotlib.pyplot as plt | |
from neuralforecast import NeuralForecast | |
from neuralforecast.models import LSTM, NHITS, RNN, PatchTST | |
data = pd.read_csv("terheles_fixed.tsv", sep="\t") | |
data['ds'] = pd.to_datetime(data['Korrigált időpont']) | |
data['y'] = data['Hatásos teljesítmény'] | |
data = data[['ds', 'y']] | |
data['unique_id'] = 1 | |
data = data[data['ds'] < '2019-09-01'] | |
Y_df = data | |
print(Y_df) | |
horizon = 4 * 24 * 7 # 7 days | |
# Try different hyperparmeters to improve accuracy. | |
models = [ | |
PatchTST(h=horizon, # Forecast horizon | |
input_size=2 * horizon, # Length of input sequence | |
max_steps=20, # Number of steps to train | |
scaler_type='standard'), # Type of scaler to normalize data | |
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 | |
] | |
''' | |
LSTM(h=horizon, # Forecast horizon | |
max_steps=100, # 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 | |
''' | |
nf = NeuralForecast(models=models, freq='15min') | |
shorter_Y_df = Y_df[Y_df['ds'] < '2019-08-01'] | |
print("-=======-") | |
print(len(shorter_Y_df)) | |
print(shorter_Y_df) | |
nf.fit(df=shorter_Y_df) | |
Y_hat_df = nf.predict() | |
print(Y_df) | |
Y_hat_df = Y_hat_df.reset_index() | |
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) | |
plot_Y_df = Y_df[Y_df['ds'] > '2019-07-01'] | |
plot_Y_df = plot_Y_df.set_index('ds')[['y']] | |
plot_Y_df.plot(ax=ax, linewidth=1) | |
Y_hat_df.set_index('ds')[['PatchTST', 'NHITS']].plot(ax=ax, linewidth=1) | |
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() | |
plt.savefig("neuralforecast.pdf") |