> ## Documentation Index
> Fetch the complete documentation index at: https://nixtla-old-docs.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# TimeMixer
Figure 1. Architecture of
SOFTS.
### Embedding
***
source
### DataEmbedding\_wo\_pos
> ```text theme={null}
> DataEmbedding_wo_pos (c_in, d_model, dropout=0.1, embed_type='fixed',
> freq='h')
> ```
*DataEmbedding\_wo\_pos*
### DFT decomposition
***
source
### DFT\_series\_decomp
> ```text theme={null}
> DFT_series_decomp (top_k)
> ```
*Series decomposition block*
### Mixing
***
source
### PastDecomposableMixing
> ```text theme={null}
> PastDecomposableMixing (seq_len, pred_len, down_sampling_window,
> down_sampling_layers, d_model, dropout,
> channel_independence, decomp_method, d_ff,
> moving_avg, top_k)
> ```
*PastDecomposableMixing*
***
source
### MultiScaleTrendMixing
> ```text theme={null}
> MultiScaleTrendMixing (seq_len, down_sampling_window,
> down_sampling_layers)
> ```
*Top-down mixing trend pattern*
***
source
### MultiScaleSeasonMixing
> ```text theme={null}
> MultiScaleSeasonMixing (seq_len, down_sampling_window,
> down_sampling_layers)
> ```
*Bottom-up mixing season pattern*
## 2. Model
***
source
### TimeMixer
> ```text theme={null}
> TimeMixer (h, input_size, n_series, stat_exog_list=None,
> hist_exog_list=None, futr_exog_list=None, d_model:int=32,
> d_ff:int=32, dropout:float=0.1, e_layers:int=4, top_k:int=5,
> decomp_method:str='moving_avg', moving_avg:int=25,
> channel_independence:int=0, down_sampling_layers:int=1,
> down_sampling_window:int=2, down_sampling_method:str='avg',
> use_norm:bool=True, decoder_input_size_multiplier:float=0.5,
> loss=MAE(), valid_loss=None, max_steps:int=1000,
> learning_rate:float=0.001, 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=32, inference_windows_batch_size=32,
> start_padding_enabled=False, step_size:int=1,
> scaler_type:str='identity', random_seed:int=1,
> drop_last_loader:bool=False, alias:Optional[str]=None,
> optimizer=None, optimizer_kwargs=None, lr_scheduler=None,
> lr_scheduler_kwargs=None, dataloader_kwargs=None,
> **trainer_kwargs)
> ```
\*TimeMixer **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]. `n_series`: int, number of
time-series. `stat_exog_list`: str list, static exogenous
columns. `hist_exog_list`: str list, historic exogenous columns.
`futr_exog_list`: str list, future exogenous columns. `d_model`:
int, dimension of the model. `d_ff`: int, dimension of the
fully-connected network. `dropout`: float, dropout rate.
`e_layers`: int, number of encoder layers. `top_k`: int, number of
selected frequencies. `decomp_method`: str, method of series
decomposition \[moving\_avg, dft\_decomp]. `moving_avg`: int, window
size of moving average. `channel_independence`: int, 0: channel
dependence, 1: channel independence. `down_sampling_layers`: int,
number of downsampling layers. `down_sampling_window`: int, size of
downsampling window. `down_sampling_method`: str, down sampling
method \[avg, max, conv]. `use_norm`: bool, whether to normalize or
not. `decoder_input_size_multiplier`: float = 0.5. `loss`:
PyTorch module, instantiated train loss class from [losses
collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).
`valid_loss`: PyTorch module=`loss`, instantiated valid loss class from
[losses
collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).
`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=32, number of windows to
sample in each training batch, default uses all.
`inference_windows_batch_size`: int=32, number of windows to sample in
each inference batch, -1 uses all. `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](https://nixtla.github.io/neuralforecast/common.scalers.html).
`random_seed`: int=1, random\_seed for pytorch initializer and numpy
generators. `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`. `dataloader_kwargs`:
dict, optional, list of parameters passed into the PyTorch Lightning
dataloader by the `TimeSeriesDataLoader`. `**trainer_kwargs`:
keyword trainer arguments inherited from [PyTorch Lighning’s
trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).
**References** [Shiyu Wang, Haixu Wu, Xiaoming Shi, Tengge Hu,
Huakun Luo, Lintao Ma, James Y. Zhang, Jun Zhou.”TimeMixer: Decomposable
Multiscale Mixing For Time Series
Forecasting”](https://openreview.net/pdf?id=7oLshfEIC2) \*
***
### TimeMixer.fit
> ```text theme={null}
> TimeMixer.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](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).
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`](https://nixtlaverse.nixtla.io/neuralforecast/tsdataset.html#timeseriesdataset),
see
[documentation](https://nixtla.github.io/neuralforecast/tsdataset.html).
`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. \*
***
### TimeMixer.predict
> ```text theme={null}
> TimeMixer.predict (dataset, test_size=None, step_size=1,
> random_seed=None, quantiles=None,
> **data_module_kwargs)
> ```
\*Predict.
Neural network prediction with PL’s `Trainer` execution of
`predict_step`.
**Parameters:** `dataset`: NeuralForecast’s
[`TimeSeriesDataset`](https://nixtlaverse.nixtla.io/neuralforecast/tsdataset.html#timeseriesdataset),
see
[documentation](https://nixtla.github.io/neuralforecast/tsdataset.html).
`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. `quantiles`: list of floats,
optional (default=None), target quantiles to predict.
`**data_module_kwargs`: PL’s TimeSeriesDataModule args, see
[documentation](https://pytorch-lightning.readthedocs.io/en/1.6.1/extensions/datamodules.html#using-a-datamodule).\*
## 3. Usage example
```python theme={null}
import pandas as pd
import matplotlib.pyplot as plt
from neuralforecast import NeuralForecast
from neuralforecast.models import TimeMixer
from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic
from neuralforecast.losses.pytorch import MAE
Y_train_df = AirPassengersPanel[AirPassengersPanel.ds=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test
model = TimeMixer(h=12,
input_size=24,
n_series=2,
scaler_type='standard',
max_steps=500,
early_stop_patience_steps=-1,
val_check_steps=5,
learning_rate=1e-3,
loss = MAE(),
valid_loss=MAE(),
batch_size=32
)
fcst = NeuralForecast(models=[model], freq='ME')
fcst.fit(df=Y_train_df, static_df=AirPassengersStatic, val_size=12)
forecasts = fcst.predict(futr_df=Y_test_df)
# Plot predictions
fig, ax = plt.subplots(1, 1, figsize = (20, 7))
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])
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['TimeMixer'], c='blue', label='median')
ax.set_title('AirPassengers Forecast', fontsize=22)
ax.set_ylabel('Monthly Passengers', fontsize=20)
ax.set_xlabel('Year', fontsize=20)
ax.legend(prop={'size': 15})
ax.grid()
```
Using `cross_validation` to forecast multiple historic values.
```python theme={null}
fcst = NeuralForecast(models=[model], freq='M')
forecasts = fcst.cross_validation(df=AirPassengersPanel, static_df=AirPassengersStatic, n_windows=2, step_size=12)
# Plot predictions
fig, ax = plt.subplots(1, 1, figsize = (20, 7))
Y_hat_df = forecasts.loc['Airline1']
Y_df = AirPassengersPanel[AirPassengersPanel['unique_id']=='Airline1']
plt.plot(Y_df['ds'], Y_df['y'], c='black', label='True')
plt.plot(Y_hat_df['ds'], Y_hat_df['TimeMixer'], c='blue', label='Forecast')
ax.set_title('AirPassengers Forecast', fontsize=22)
ax.set_ylabel('Monthly Passengers', fontsize=20)
ax.set_xlabel('Year', fontsize=20)
ax.legend(prop={'size': 15})
ax.grid()
```
