The growing number of literary works being produced and published has emphasised the importance of better cataloguing methods to handle the increasing volume effectively. One specific issue is the lack of organising works by time periods, which is crucial for understanding and organising literature. In this study, "time" refers to when the story's events occur or the narrative's temporal setting, like specific historical periods or events, rather than the publication date. Categorising literary works based on their historical settings can significantly improve accessibility for library patrons navigating online catalogues. However, time period categorisation is uncommon, primarily due to the resource-intensive nature of the process, which necessitates extensive analysis by librarians and cataloguers. To address this issue, this paper proposes evaluating different machine learning workflows to predict time periods for novels. The workflow comprises preprocessing, feature engineering, classification, and evaluation. The feature engineering techniques used are Latent Dirichlet Allocation (LDA), Word Embedding with Sentence-BERT (WE SBERT), and Term Frequency-Inverse Document Frequency (TF-IDF), and the classification algorithm used is Logistic Regression. The models are assessed using the F1 score, precision, and recall metrics. The time period categories used are Medieval, Era of Great Power, Age of Liberty, and Gustavian periods. The objective is to determine how effectively each model categorises Swedish historical fiction novels into their appropriate time period categories. By leveraging machine learning techniques, the research seeks to supplement the time period categorisation process, aiding cataloguers and ultimately enhancing the accessibility and usability of library collections.