Using multiple covariates in LSTM model throws error when fitting.

Describe the bug When adding the list of covariates I get this error : ValueError: The provided sequence of target series must have the same length as the provided sequence of covariate series.

To Reproduce

import pandas as pd
import numpy as np
from darts import TimeSeries
from darts.models import RNNModel
from darts.dataprocessing.transformers import Scaler

# Create toy dataset
idx = pd.date_range(start=pd.Timestamp('2020-01-01'), freq='MS', periods=10 )
df = pd.DataFrame({
    'target':np.random.randint(100, 10000, size=10,),
    'var1' :np.random.randint(100, 10000, size=10,),
    'var2' :np.random.randint(100, 10000, size=10,),
    'var3' :np.random.randint(100, 10000, size=10,)
}, index= idx)

# Creating Time series target and variables
series_target = TimeSeries.from_dataframe(df,value_cols=['target'])
series_var1 = TimeSeries.from_dataframe(df,value_cols=['var1'])
series_var2 = TimeSeries.from_dataframe(df, value_cols = ['var2'])
series_var3 = TimeSeries.from_dataframe(df, value_cols = ['var3'])

# Scaling the series
scaler_target = Scaler()
scaler_var1 =  Scaler()
scaler_var2 = Scaler()
scaler_var3 = Scaler()
series_target_scaled = scaler_target.fit_transform(series_target)
series_var1_scaled = scaler_var1.fit_transform(series_var1)
series_var2_scaled = scaler_var2.fit_transform(series_var2)
series_var3_scaled = scaler_var3.fit_transform(series_var3)

# Training and Validation Split
split_time = pd.Timestamp('2020-07-01')
train_target, val_target = series_target_scaled.split_after(split_time) 
train_var1, val_var1 = series_var1_scaled.split_after(split_time) 
train_var2, val_var2 = series_var2_scaled.split_after(split_time) 
train_var3, val_var3 = series_var3_scaled.split_after(split_time) 

# Create model
model = RNNModel(
    model='LSTM',
    input_chunk_length=6,
    output_chunk_length=3,
    hidden_size=32,
    n_rnn_layers=1,
    dropout=0.2,
    batch_size=8,
    n_epochs=200,
    optimizer_kwargs={'lr': 1e-3},
    model_name='test_lstm',
    log_tensorboard=True,
    random_state=42
)

# Fitting with covariates  (error here)
model.fit(series=train_target, 
          covariates=[train_var1, train_var2, train_var3], 
          verbose=True)

Expected behavior Model to fit so I can later predict using:

# Predicting With Covariate 
pred_cov = model.predict(n=3, 
                         series= train_target, 
                         covariates= [train_var1, train_var2 , train_var2 ])

System (please complete the following information):

• Python version: [e.g. Python 3.7.10]
• darts version --> I’m not sure but I used !pip install 'u8darts[all]'

Additional context I’m unsure about the difference of covariate vs. multivariate. To make it work, I have to fit the same number of series and covariates in the .fit() and .predict() I only care about predicting the target and only using var1, var2, var3 to help predict the target.

[hrzn]

Hi @phellstrand77, there are several ways to build a multivariate series. For example:

• From a Pandas DataFrame containing multiple columns (one per dimension), you can do TimeSeries.from_dataframe(df, time_col, value_cols) where value_cols is the list of columns that you want to integrate.
• From several univariate TimeSeries, you can do

from darts import concatenate
my_multivariate_series = concatenate([series1, series2, ...], axis=1)

I hope this helps.

[phellstrand77]

@hrzn thank you very much, I now understand the constraints.