title: Understanding Time2Vec embedding for implementing this as a keras layer
body: <p>The paper time2vector <a href="https://arxiv.org/pdf/1907.05321.pdf#section.4" rel="nofollow noreferrer">link</a> (the relevant theory is in section 4) shows an approach to include a time embedding for features to improve model performance. I would like to give this a try. I found a implementation as keras layer which I changed a little bit. Basically it creates two matrices for one feature:</p>
  <p>(1) linear = w * x + b</p>
  <p>(2) periodic = sin(w * x + b)</p>
  <p>Currently I choose this feature manually. Concerning the paper there are a few things i don't understand. The first thing is the term k as the number of sinusoids. The authors use up to 64 sinusoids. What does this mean? I have just 1 sinusoid at the moment, right? Secondly I'm about to put every feature I have through the sinus transformation for me dataset that would make 6 (sinusoids) periodic features. The authors use only one linear term. How should I choose the feature for the linear term? Unfortunately the code from the paper is not available anymore. Has anyone worked with time embeddings or even with this particularly approach?</p>