201 lines
7.7 KiB
Python
201 lines
7.7 KiB
Python
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import pandas as pd
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from datasetsforecast.long_horizon import LongHorizon
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# Change this to your own data to try the model
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Y_df, _, _ = LongHorizon.load(directory='./', group='ETTm2')
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Y_df['ds'] = pd.to_datetime(Y_df['ds'])
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# For this excercise we are going to take 20% of the DataSet
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n_time = len(Y_df.ds.unique())
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val_size = int(.2 * n_time)
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test_size = int(.2 * n_time)
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Y_df.groupby('unique_id').head(2)
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import matplotlib.pyplot as plt
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# We are going to plot the temperature of the transformer
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# and marking the validation and train splits
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u_id = 'HUFL'
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x_plot = pd.to_datetime(Y_df[Y_df.unique_id==u_id].ds)
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y_plot = Y_df[Y_df.unique_id==u_id].y.values
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x_val = x_plot[n_time - val_size - test_size]
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x_test = x_plot[n_time - test_size]
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fig = plt.figure(figsize=(10, 5))
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fig.tight_layout()
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plt.plot(x_plot, y_plot)
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plt.xlabel('Date', fontsize=17)
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plt.ylabel('HUFL [15 min temperature]', fontsize=17)
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plt.axvline(x_val, color='black', linestyle='-.')
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plt.axvline(x_test, color='black', linestyle='-.')
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plt.text(x_val, 5, ' Validation', fontsize=12)
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plt.text(x_test, 5, ' Test', fontsize=12)
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plt.grid()
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from ray import tune
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from neuralforecast.auto import AutoNHITS
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from neuralforecast.core import NeuralForecast
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horizon = 96 # 24hrs = 4 * 15 min.
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# Use your own config or AutoNHITS.default_config
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nhits_config = {
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"learning_rate": tune.choice([1e-3]), # Initial Learning rate
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"max_steps": tune.choice([1000]), # Number of SGD steps
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"input_size": tune.choice([5 * horizon]), # input_size = multiplier * horizon
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"batch_size": tune.choice([7]), # Number of series in windows
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"windows_batch_size": tune.choice([256]), # Number of windows in batch
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"n_pool_kernel_size": tune.choice([[2, 2, 2], [16, 8, 1]]), # MaxPool's Kernelsize
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"n_freq_downsample": tune.choice([[168, 24, 1], [24, 12, 1], [1, 1, 1]]), # Interpolation expressivity ratios
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"activation": tune.choice(['ReLU']), # Type of non-linear activation
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"n_blocks": tune.choice([[1, 1, 1]]), # Blocks per each 3 stacks
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"mlp_units": tune.choice([[[512, 512], [512, 512], [512, 512]]]), # 2 512-Layers per block for each stack
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"interpolation_mode": tune.choice(['linear']), # Type of multi-step interpolation
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"val_check_steps": tune.choice([100]), # Compute validation every 100 epochs
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"random_seed": tune.randint(1, 10),
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}
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tft_config = {
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"input_size": tune.choice([horizon]),
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"hidden_size": tune.choice([32]),
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"n_head": tune.choice([2]),
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"learning_rate": tune.loguniform(1e-4, 1e-1),
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"scaler_type": tune.choice(['robust', 'standard']),
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"max_steps": tune.choice([500, 1000]),
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"windows_batch_size": tune.choice([32]),
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"check_val_every_n_epoch": tune.choice([100]),
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"random_seed": tune.randint(1, 20),
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}
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tsmixer_config = {
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"input_size": input_size, # Size of input window
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"max_steps": tune.choice([500, 1000, 2000]), # Number of training iterations
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"val_check_steps": 100, # Compute validation every x steps
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"early_stop_patience_steps": 5, # Early stopping steps
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"learning_rate": tune.loguniform(1e-4, 1e-2), # Initial Learning rate
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"n_block": tune.choice([1, 2, 4, 6, 8]), # Number of mixing layers
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"dropout": tune.uniform(0.0, 0.99), # Dropout
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"ff_dim": tune.choice([32, 64, 128]), # Dimension of the feature linear layer
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"scaler_type": 'identity',
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}
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tsmixerx_config = tsmixer_config.copy()
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tsmixerx_config['futr_exog_list'] = ['ex_1', 'ex_2', 'ex_3', 'ex_4']
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models = [AutoNHITS(h=horizon,
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config=nhits_config,
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num_samples=5),
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AutoTFT(h=horizon,
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loss=MAE(),
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config=tft_config,
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num_samples=3),
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TSMixer(h=horizon,
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input_size=input_size,
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n_series=7,
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max_steps=1000,
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val_check_steps=100,
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early_stop_patience_steps=5,
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scaler_type='identity',
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valid_loss=MAE(),
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random_seed=12345678,
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),
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TSMixerx(h=horizon,
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input_size=input_size,
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n_series=7,
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max_steps=1000,
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val_check_steps=100,
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early_stop_patience_steps=5,
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scaler_type='identity',
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dropout=0.7,
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valid_loss=MAE(),
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random_seed=12345678,
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futr_exog_list=['ex_1', 'ex_2', 'ex_3', 'ex_4'],
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),
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MLPMultivariate(h=horizon,
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input_size=input_size,
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n_series=7,
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max_steps=1000,
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val_check_steps=100,
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early_stop_patience_steps=5,
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scaler_type='standard',
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hidden_size=256,
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valid_loss=MAE(),
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random_seed=12345678,
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),
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NHITS(h=horizon,
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input_size=horizon,
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max_steps=1000,
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val_check_steps=100,
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early_stop_patience_steps=5,
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scaler_type='robust',
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valid_loss=MAE(),
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random_seed=12345678,
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),
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AutoTSMixer(h=horizon,
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n_series=7,
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loss=MAE(),
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config=tsmixer_config,
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num_samples=10,
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search_alg=HyperOptSearch(),
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backend='ray',
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valid_loss=MAE()) ,
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AutoTSMixerx(h=horizon,
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n_series=7,
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loss=MAE(),
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config=tsmixerx_config,
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num_samples=10,
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search_alg=HyperOptSearch(),
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backend='ray',
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valid_loss=MAE()) ]
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nf = NeuralForecast(
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models=models,
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freq='15min')
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Y_hat_df = nf.cross_validation(df=Y_df, val_size=val_size,
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test_size=test_size, n_windows=None)
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nf.models[0].results.get_best_result().config
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y_true = Y_hat_df.y.values
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y_hat = Y_hat_df['AutoNHITS'].values
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n_series = len(Y_df.unique_id.unique())
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y_true = y_true.reshape(n_series, -1, horizon)
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y_hat = y_hat.reshape(n_series, -1, horizon)
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print('Parsed results')
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print('2. y_true.shape (n_series, n_windows, n_time_out):\t', y_true.shape)
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print('2. y_hat.shape (n_series, n_windows, n_time_out):\t', y_hat.shape)
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fig, axs = plt.subplots(nrows=3, ncols=1, figsize=(10, 11))
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fig.tight_layout()
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series = ['HUFL','HULL','LUFL','LULL','MUFL','MULL','OT']
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series_idx = 3
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for idx, w_idx in enumerate([200, 300, 400]):
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axs[idx].plot(y_true[series_idx, w_idx,:],label='True')
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axs[idx].plot(y_hat[series_idx, w_idx,:],label='Forecast')
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axs[idx].grid()
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axs[idx].set_ylabel(series[series_idx]+f' window {w_idx}',
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fontsize=17)
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if idx==2:
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axs[idx].set_xlabel('Forecast Horizon', fontsize=17)
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plt.legend()
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plt.show()
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plt.close()
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from neuralforecast.losses.numpy import mae, mse
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print('MAE: ', mae(y_hat, y_true))
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print('MSE: ', mse(y_hat, y_true))
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