Seasonality is an important dimension for relevance in e-commerce search. For example, a query jacket has a different set of relevant documents in winter than summer. For an optimal user experience, the e-commerce search engines should incorporate seasonality in product search. In this paper, we formally introduce the concept of seasonal relevance, define it and quantify using data from a major e-commerce store. In our analyses, we find 39% queries are highly seasonally relevant to the time of search and would benefit from handling seasonality in ranking. We propose LogSR and VelSR features to capture product seasonality using state-of-the-art neural models based on self-attention. Comprehensive offline and online experiments over large datasets show the efficacy of our methods to model seasonal relevance. The online A/B test on 784 MM queries shows the treatment with seasonal relevance features results in 2.20% higher purchases and better customer experience overall.