Recommendation systems are algorithms for suggesting relevant items to users. Generally, the recommendations are expressed in what will be recommended and a value representing the recommendation’s relevance. However, forecasting if the user will buy the recommended item in the next day, week, or month is crucial for companies. The present study describes a process to suggest items from sequential patterns under temporal restrictions. The novelty in our proposal is that the recommendation considers the time when the item will be acquired by proposing a notation grouping items occurring in the same time window. Therefore, our algorithm could predict the itemsets in the next time windows. The document formalizes the recommendation process composed of a time constraint frequent sequential patterns and a structure for predicting the next itemset based on time constrained frequent patterns. Therefore, to validate our proposal, the prediction results are measured in terms of precision and Jaccard index for three different real datasets about credit and debit card transactions, online shopping, and mobile phone App. Our findings demonstrate the pertinence and relevance of our approach.