I am trying to implement NBeats model from keras-beats for timeseries prediction forcasting and it shows me valueerror about using float data type. I tried using astype to convert data to float 32 and 64 and it still showed me similar error

code:
from kerasbeats import prep_time_series, NBeatsModel

# import the dataset

df = pd.read_csv(‘DailyDelhiClimateTrain.csv’, parse_dates = [‘date’], index_col = ‘date’)

# sort by dates

df.sort_index(inplace = True)

# prep a univariate time series for N-Beats

X, y = prep_time_series(df[‘meantemp’], lookback = 7, horizon = 1)

# create training and test sets

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y,

shuffle = False, test_size = 0.2)

# initialize N-Beats and fit

nbeats = NBeatsModel(model_type = ‘generic’, lookback = 7, horizon = 1)

nbeats.fit(X,y)

Error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[104], line 4
      1 X = X.astype('float64')
      2 y = y.astype('float64')
----> 4 nbeats.fit(X,y)

File ~AppDataRoamingPythonPython311site-packageskerasbeatsnbeats.py:384, in NBeatsModel.fit(self, X, y, **kwargs)
    382 """Build and fit model"""
    383 self.build_layer()
--> 384 self.build_model()
    385 self.model.compile(optimizer = keras.optimizers.Adam(self.learning_rate), 
    386                    loss      = [self.loss],
    387                    metrics   = ['mae', 'mape'])
    388 self.model.fit(X, y, batch_size = self.batch_size, **kwargs)

File ~AppDataRoamingPythonPython311site-packageskerasbeatsnbeats.py:376, in NBeatsModel.build_model(self)
    374 def build_model(self):
    375     """Creates keras model to use for fitting"""
--> 376     inputs     = keras.layers.Input(shape = (self.horizon * self.lookback, ), dtype = 'float')
    377     forecasts  = self.model_layer(inputs)
    378     self.model = Model(inputs, forecasts)

File ~AppDataRoamingPythonPython311site-packageskerassrclayerscoreinput_layer.py:143, in Input(shape, batch_size, dtype, sparse, batch_shape, name, tensor)
     89 @keras_export(["keras.layers.Input", "keras.Input"])
     90 def Input(
     91     shape=None,
   (...)
     97     tensor=None,
     98 ):
     99     """Used to instantiate a Keras tensor.
    100 
    101     A Keras tensor is a symbolic tensor-like object, which we augment with
   (...)
    141     ```
    142     """
--> 143     layer = InputLayer(
    144         shape=shape,
    145         batch_size=batch_size,
    146         dtype=dtype,
    147         sparse=sparse,
    148         batch_shape=batch_shape,
    149         name=name,
    150         input_tensor=tensor,
    151     )
    152     return layer.output

File ~AppDataRoamingPythonPython311site-packageskerassrclayerscoreinput_layer.py:49, in InputLayer.__init__(self, shape, batch_size, dtype, sparse, batch_shape, input_tensor, name, **kwargs)
     47     batch_shape = (batch_size,) + shape
     48 self.batch_shape = tuple(batch_shape)
---> 49 self._dtype = backend.standardize_dtype(dtype)
     51 self.sparse = bool(sparse)
     52 if self.sparse and not backend.SUPPORTS_SPARSE_TENSORS:

File ~AppDataRoamingPythonPython311site-packageskerassrcbackendcommonvariables.py:521, in standardize_dtype(dtype)
    518     dtype = dtype.__name__
    520 if dtype not in dtypes.ALLOWED_DTYPES:
--> 521     raise ValueError(f"Invalid dtype: {dtype}")
    522 return dtype

ValueError: Invalid dtype: float

I tried changing dtype to float 32 and 64 and it still didnt work. I was just expecting model to fit the data. For data I have used climate change daata available at kaggle