<p>Generally speaking, you are correct. The input shape should be (window length, features n).</p>

<p>However, there has been some success in transforming the input to the way you describe above. Below is a whitepaper where they were able to beat many top performing algorithms by doing so, and they used convolutional 1D layers to handle the time series pattern through a separate input.</p>

<p><a href="https://arxiv.org/pdf/1709.05206.pdf" rel="nofollow noreferrer">LSTM Fully Convolutional Networks for Time
Series Classification</a></p>
Wrong input shape for lstm keras
<p>I came across a tutorial where the autor use a LSTM network for a time series prediction like this :</p>



<pre class="lang-python prettyprint-override"><code>trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))

model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
</code></pre>

<p>We agree that the LSTM in this act like a normal NN (and is useless ?) since the LSTM got only one time step without stateful = TRUE , Am I right ?</p>