What arguments should I pass to input_shape parameter of LSTM function in Keras?
Following example shows that by printing shape of your train data you can pass the right value to input_shape(). Also it depends you want to feed your data in 2D or another way. As it can be seen in snippet:

trainX.shape[1]=10

trainX.shape[2]=1440 

and in output based on test shape Dense(units = 960)

#Shape of train and test data
trainX, testX, trainY, testY = train_test_split(trainX,trainY, test_size=0.2 , shuffle=False)
print("train size: {}".format(trainX.shape))
print("train Label size: {}".format(trainY.shape))
print("test size: {}".format(testX.shape))
print("test Label size: {}".format(testY.shape))
#train size: (23, 10, 1440)
#train Label size: (23, 960)
#test size: (6, 10, 1440)
#test Label size: (6, 960)

# create and fit the Simple LSTM model 

#model_LSTM = Sequential()
model_LSTM.add(LSTM(units = 1440, input_shape=(trainX.shape[1], trainX.shape[2])))
model_LSTM.add(Dense(units = 960))



Edit: Following tips based on this example to help you when preparing your input data for LSTMs:


The LSTM input layer must be 3D.
The meaning of the 3 input dimensions are: samples, time steps, and features.
The LSTM input layer is defined by the input_shape argument on the first hidden layer.
The input_shape argument takes a tuple of two values that define the number of time steps and features.
The number of samples is assumed to be 1 or more.
The reshape() function on NumPy arrays can be used to reshape your 1D or 2D data to be 3D.
The reshape() function takes a tuple as an argument that defines the new shape.