# What reccent alternatives to LSTM are there for regression problems?

I have been working for a while on a regression problem - predicting the air pollution in a city based on meteorological features (humidity, temperature, wind velocity a.o.). I have trained an LSTM model to do the predictions and it works somehow fine, but I am not impressed - the trend in my predictions is always lagging behind the trend in the ground truth. LSTMs are more than 20 years old so I wondered if there are some new alternatives for regression problems of the kind I am working on? I have read that attention models are popular for text prediction, but are they also suitable for air pollution prediction? What else is there? Especially models that are implemented in TensorFlow and Keras would be of interest for me. Thanks!

There is a lot of active research in making versions of the Transformer that scale to larger values of N. If you read the papers, the claims of O(N) or O(NlogN) are a little misleading as they end up batching things together for efficiency; but still they are managing to work with sequence lengths in the thousands or higher.