lstm prediction result delay phenomenon

Recently I'm using lstm to predict time series. I'm using keras 2.0 to construct my lstm model. It has a structure like this:

model = Sequential()
model.add(LSTM(128, input_shape=(timesteps, 1), return_sequences=False, stateful=False)
model.add(Dropout(rate=0.1))
model.add(Dense(1))

I have tried to use this network to predict several time series including sin(t) and a real traffic flow dataset. I found that the prediction for sin is fine while the prediction for real dataset is just like shifting the last input value by one step. I don't know whether it's a prediction error or the network doesn't learn the pattern of the dataset at all. Does anyone get similar results? Are there any solutions to this annoying shift? Thanks a lot. Here are some of my predictions:

3 frequencies sin prediction result

real traffic dataset prediction result

This is simply the starting point for your network and you'll have to work through it by trying various things.

To name only a few:

• Try different window lengths (timesteps fed into network)
• Try adding dense layers, or multiple LSTM layers, or fewer LTSM nodes
• Try different optimizers, with various learning rates
• Look for additional datapoints to feed into the network
• How much data do you have? You may need more to get a good prediction
• Try different offsets for the Y variable, how many timesteps do you need to be able to predict out for your specific problem?

The list goes on....