I am making a model for predicting the network traffic volume for our data center. Let me describe my dataset first. At this time, we have the model of 90 days, on each day, we record the network traffic volume every minute. And this data also has the seasonality pattern: the network traffic volume fluctuates and remains at night, decreases in the morning and increases in the afternoon (as I could observe, this pattern happens on all the data in my dataset)
What I want at this time is a model for predicting the network traffic volume in some next minutes(25 minutes, for example), given that the data is given for all the previous minutes. The new predicted value will be contributed for the next predicting. For example, the value at minute i will be added to a window of data with a specific length to predict the value at minute i+1.
At this time, I have tried LSTM-RNN and the feature I use is the minute of the day and the network traffic volume in that minute (with normalization before inputting to the LSTM network). However, my problem is: my model could catch the wrong pattern of the data: when the network traffic volume should increase in the afternoon, my model predicts it decreases. I have tried with different structures of LSTM network (increases the LSTMs layers, change the number of nodes in the fully-connected layers after the LSTM layers...); and also the length of the window, but that problem still remains.
So I want to ask if there is any problem with LSTM model for predicting the data with seasonal trend like my data? If no, could anybody suggest me an LSTM model or any other suitable model for my data?
Thank you in advance :-)