title: inverse transform throws error in LSTM prediction
body: <p>I am having a LSTM which will predict the  value for the next timestep .So my inputs are scaled into the LSTM like below:</p>
  
  <pre><code>test_X = reframed.values
  test_X = test_X[:test_X.shape[0], :]
  scaler = PowerTransformer()
  test_X = scaler.fit_transform(test_X)
  test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
  print(test_X.shape) 
  </code></pre>
  
  <blockquote>
    <p>(1, 1, 17)</p>
  </blockquote>
  
  <p>Then I predict like below :</p>
  
  <pre><code>yhat = model.predict(test_X)
  </code></pre>
  
  <p>And I have a value for that .</p>
  
  <p>Now I want to inverse scale it so I apply </p>
  
  <pre><code>inv_yhat = scaler.inverse_transform(yhat)
  </code></pre>
  
  <p>But this throws an error like below :</p>
  
  <blockquote>
    <p>ValueError: Input data has a different number of features than fitting
    data. Should have 17, data has 1</p>
  </blockquote>
  
  <p>Not sure why is this error throwing when the model already predicted the value but just inverse scaling is left.</p>
  
  <p>EDIT :</p>
  
  <p>I use the scalers like this in my training </p>
  
  <pre><code>scaler_x = PowerTransformer()
  scaler_y = PowerTransformer()
  train_X = scaler_x.fit_transform(train_X)
  train_y = scaler_y.fit_transform(train_y.reshape(-1,1))
  test_X = scaler_x.fit_transform(test_X)
  test_y = scaler_y.fit_transform(test_y.reshape(-1,1))
  print(train_X.shape, train_y.shape, test_X.shape, test_y.shape) 
  
  
  
  import joblib
  joblib.dump(scaler_x, 'scaler_x.gz')
  joblib.dump(scaler_y, 'scaler_y.gz')
  </code></pre>
  
  <p>Then I scale my new fetaures like below :</p>
  
  <pre><code>scaler = joblib.load('scaler.gz')
  test_X =scaler.transform(test_X)
  </code></pre>
  
  <p>still throwing the same error.</p>
  
  <p>Any help is appreciated.</p>