I am working on a project for predicting the number of DNS queries from the site: DNS queries statistics. The data I use is minutely data, which means the number of DNS queries of every minute.
If you look at the number of DNS queries from South Korea or any other countries, it has the seasonality and trend characteristics: increase then decrease day by day.
The requirement for me is: given the number of DNS queries of every minute, then for the given data of 20 previous minutes, predict the number of DNS queries in the next 20 minutes.
My problem is: my trained LSTM could not detect these factors, it always predicts my data to decrease everytime.
I have employed some hand-defined features like the minute of the day, period of the day like morning/afternoon... But it still keeps the same problem.
So I want to ask if there is any possible improvement to make my LSTM to understand when it should decrease and when it should increase with the seasonal data?