PatchTST
The PatchTST model is an efficient Transformer-based model for multivariate time series forecasting.
It is based on two key components: - segmentation of time series into windows (patches) which are served as input tokens to Transformer - channel-independence. where each channel contains a single univariate time series.
1. Backbone
Auxiliary Functions
source
get_activation_fn
get_activation_fn (activation)
source
Transpose
Transpose (*dims, contiguous=False)
Transpose
Positional Encoding
source
positional_encoding
positional_encoding (pe, learn_pe, q_len, hidden_size)
source
Coord1dPosEncoding
Coord1dPosEncoding (q_len, exponential=False, normalize=True)
source
Coord2dPosEncoding
Coord2dPosEncoding (q_len, hidden_size, exponential=False, normalize=True, eps=0.001)
source
PositionalEncoding
PositionalEncoding (q_len, hidden_size, normalize=True)
Encoder
source
TSTEncoderLayer
TSTEncoderLayer (q_len, hidden_size, n_heads, d_k=None, d_v=None, linear_hidden_size=256, store_attn=False, norm='BatchNorm', attn_dropout=0, dropout=0.0, bias=True, activation='gelu', res_attention=False, pre_norm=False)
TSTEncoderLayer
source
TSTEncoder
TSTEncoder (q_len, hidden_size, n_heads, d_k=None, d_v=None, linear_hidden_size=None, norm='BatchNorm', attn_dropout=0.0, dropout=0.0, activation='gelu', res_attention=False, n_layers=1, pre_norm=False, store_attn=False)
TSTEncoder
source
TSTiEncoder
TSTiEncoder (c_in, patch_num, patch_len, max_seq_len=1024, n_layers=3, hidden_size=128, n_heads=16, d_k=None, d_v=None, linear_hidden_size=256, norm='BatchNorm', attn_dropout=0.0, dropout=0.0, act='gelu', store_attn=False, key_padding_mask='auto', padding_var=None, attn_mask=None, res_attention=True, pre_norm=False, pe='zeros', learn_pe=True)
TSTiEncoder
source
Flatten_Head
Flatten_Head (individual, n_vars, nf, h, c_out, head_dropout=0)
Flatten_Head
source
PatchTST_backbone
PatchTST_backbone (c_in:int, c_out:int, input_size:int, h:int, patch_len:int, stride:int, max_seq_len:Optional[int]=1024, n_layers:int=3, hidden_size=128, n_heads=16, d_k:Optional[int]=None, d_v:Optional[int]=None, linear_hidden_size:int=256, norm:str='BatchNorm', attn_dropout:float=0.0, dropout:float=0.0, act:str='gelu', key_padding_mask:str='auto', padding_var:Optional[int]=None, attn_mask:Optional[torch.Tensor]=None, res_attention:bool=True, pre_norm:bool=False, store_attn:bool=False, pe:str='zeros', learn_pe:bool=True, fc_dropout:float=0.0, head_dropout=0, padding_patch=None, pretrain_head:bool=False, head_type='flatten', individual=False, revin=True, affine=True, subtract_last=False)
PatchTST_backbone
2. Model
source
PatchTST
PatchTST (h, input_size, stat_exog_list=None, hist_exog_list=None, futr_exog_list=None, exclude_insample_y=False, encoder_layers:int=3, n_heads:int=16, hidden_size:int=128, linear_hidden_size:int=256, dropout:float=0.2, fc_dropout:float=0.2, head_dropout:float=0.0, attn_dropout:float=0.0, patch_len:int=16, stride:int=8, revin:bool=True, revin_affine:bool=False, revin_subtract_last:bool=True, activation:str='gelu', res_attention:bool=True, batch_normalization:bool=False, learn_pos_embed:bool=True, loss=MAE(), valid_loss=None, max_steps:int=5000, learning_rate:float=0.0001, num_lr_decays:int=-1, early_stop_patience_steps:int=-1, val_check_steps:int=100, batch_size:int=32, valid_batch_size:Optional[int]=None, windows_batch_size=1024, inference_windows_batch_size:int=1024, start_padding_enabled=False, step_size:int=1, scaler_type:str='identity', random_seed:int=1, num_workers_loader:int=0, drop_last_loader:bool=False, optimizer=None, optimizer_kwargs=None, lr_scheduler=None, lr_scheduler_kwargs=None, **trainer_kwargs)
*PatchTST
The PatchTST model is an efficient Transformer-based model for multivariate time series forecasting.
It is based on two key components: - segmentation of time series into windows (patches) which are served as input tokens to Transformer - channel-independence, where each channel contains a single univariate time series.
Parameters:
h
: int, Forecast horizon.
input_size
: int,
autorregresive inputs size, y=[1,2,3,4] input_size=2 ->
y_[t-2:t]=[1,2].
stat_exog_list
: str list, static exogenous
columns.
hist_exog_list
: str list, historic exogenous columns.
futr_exog_list
: str list, future exogenous columns.
exclude_insample_y
: bool=False, the model skips the autoregressive
features y[t-input_size:t] if True.
encoder_layers
: int, number
of layers for encoder.
n_heads
: int=16, number of multi-head’s
attention.
hidden_size
: int=128, units of embeddings and
encoders.
linear_hidden_size
: int=256, units of linear layer.
dropout
: float=0.1, dropout rate for residual connection.
fc_dropout
: float=0.1, dropout rate for linear layer.
head_dropout
: float=0.1, dropout rate for Flatten head layer.
attn_dropout
: float=0.1, dropout rate for attention layer.
patch_len
: int=32, length of patch. Note: patch_len = min(patch_len,
input_size + stride).
stride
: int=16, stride of patch.
revin
: bool=True, bool to use RevIn.
revin_affine
: bool=False,
bool to use affine in RevIn.
revin_substract_last
: bool=False,
bool to use substract last in RevIn.
activation
: str=‘ReLU’,
activation from [‘gelu’,‘relu’].
res_attention
: bool=False, bool
to use residual attention.
batch_normalization
: bool=False, bool
to use batch normalization.
learn_pos_embedding
: bool=True, bool
to learn positional embedding.
loss
: PyTorch module, instantiated
train loss class from losses
collection.
valid_loss
: PyTorch module=loss
, instantiated valid loss class from
losses
collection.
max_steps
: int=1000, maximum number of training steps.
learning_rate
: float=1e-3, Learning rate between (0, 1).
num_lr_decays
: int=-1, Number of learning rate decays, evenly
distributed across max_steps.
early_stop_patience_steps
: int=-1,
Number of validation iterations before early stopping.
val_check_steps
: int=100, Number of training steps between every
validation loss check.
batch_size
: int=32, number of different
series in each batch.
valid_batch_size
: int=None, number of
different series in each validation and test batch, if None uses
batch_size.
windows_batch_size
: int=1024, number of windows to
sample in each training batch, default uses all.
inference_windows_batch_size
: int=1024, number of windows to sample in
each inference batch.
start_padding_enabled
: bool=False, if True,
the model will pad the time series with zeros at the beginning, by input
size.
step_size
: int=1, step size between each window of temporal
data.
scaler_type
: str=‘identity’, type of scaler for temporal
inputs normalization see temporal
scalers.
random_seed
: int, random_seed for pytorch initializer and numpy
generators.
num_workers_loader
: int=os.cpu_count(), workers to be
used by TimeSeriesDataLoader
.
drop_last_loader
: bool=False, if
True TimeSeriesDataLoader
drops last non-full batch.
alias
: str,
optional, Custom name of the model.
optimizer
: Subclass of
‘torch.optim.Optimizer’, optional, user specified optimizer instead of
the default choice (Adam).
optimizer_kwargs
: dict, optional, list
of parameters used by the user specified optimizer
.
lr_scheduler
: Subclass of ‘torch.optim.lr_scheduler.LRScheduler’,
optional, user specified lr_scheduler instead of the default choice
(StepLR).
lr_scheduler_kwargs
: dict, optional, list of parameters
used by the user specified lr_scheduler
.
**trainer_kwargs
: int, keyword trainer arguments inherited from
PyTorch Lighning’s
trainer.
PatchTST.fit
PatchTST.fit (dataset, val_size=0, test_size=0, random_seed=None, distributed_config=None)
*Fit.
The fit
method, optimizes the neural network’s weights using the
initialization parameters (learning_rate
, windows_batch_size
, …) and
the loss
function as defined during the initialization. Within fit
we use a PyTorch Lightning Trainer
that inherits the initialization’s
self.trainer_kwargs
, to customize its inputs, see PL’s trainer
arguments.
The method is designed to be compatible with SKLearn-like classes and in particular to be compatible with the StatsForecast library.
By default the model
is not saving training checkpoints to protect
disk memory, to get them change enable_checkpointing=True
in
__init__
.
Parameters:
dataset
: NeuralForecast’s
TimeSeriesDataset
,
see
documentation.
val_size
: int, validation size for temporal cross-validation.
random_seed
: int=None, random_seed for pytorch initializer and numpy
generators, overwrites model.__init__’s.
test_size
: int, test
size for temporal cross-validation.
*
PatchTST.predict
PatchTST.predict (dataset, test_size=None, step_size=1, random_seed=None, **data_module_kwargs)
*Predict.
Neural network prediction with PL’s Trainer
execution of
predict_step
.
Parameters:
dataset
: NeuralForecast’s
TimeSeriesDataset
,
see
documentation.
test_size
: int=None, test size for temporal cross-validation.
step_size
: int=1, Step size between each window.
random_seed
:
int=None, random_seed for pytorch initializer and numpy generators,
overwrites model.__init__’s.
**data_module_kwargs
: PL’s
TimeSeriesDataModule args, see
documentation.*
Usage example
import pandas as pd
import matplotlib.pyplot as plt
from neuralforecast import NeuralForecast
from neuralforecast.models import PatchTST
from neuralforecast.losses.pytorch import DistributionLoss
from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic, augment_calendar_df
AirPassengersPanel, calendar_cols = augment_calendar_df(df=AirPassengersPanel, freq='M')
Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]] # 132 train
Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test
model = PatchTST(h=12,
input_size=104,
patch_len=24,
stride=24,
revin=False,
hidden_size=16,
n_heads=4,
scaler_type='robust',
loss=DistributionLoss(distribution='StudentT', level=[80, 90]),
#loss=MAE(),
learning_rate=1e-3,
max_steps=500,
val_check_steps=50,
early_stop_patience_steps=2)
nf = NeuralForecast(
models=[model],
freq='M'
)
nf.fit(df=Y_train_df, static_df=AirPassengersStatic, val_size=12)
forecasts = nf.predict(futr_df=Y_test_df)
Y_hat_df = forecasts.reset_index(drop=False).drop(columns=['unique_id','ds'])
plot_df = pd.concat([Y_test_df, Y_hat_df], axis=1)
plot_df = pd.concat([Y_train_df, plot_df])
if model.loss.is_distribution_output:
plot_df = plot_df[plot_df.unique_id=='Airline1'].drop('unique_id', axis=1)
plt.plot(plot_df['ds'], plot_df['y'], c='black', label='True')
plt.plot(plot_df['ds'], plot_df['PatchTST-median'], c='blue', label='median')
plt.fill_between(x=plot_df['ds'][-12:],
y1=plot_df['PatchTST-lo-90'][-12:].values,
y2=plot_df['PatchTST-hi-90'][-12:].values,
alpha=0.4, label='level 90')
plt.grid()
plt.legend()
plt.plot()
else:
plot_df = plot_df[plot_df.unique_id=='Airline1'].drop('unique_id', axis=1)
plt.plot(plot_df['ds'], plot_df['y'], c='black', label='True')
plt.plot(plot_df['ds'], plot_df['PatchTST'], c='blue', label='Forecast')
plt.legend()
plt.grid()