A multi-period planning framework is proposed that exploits multi-step ahead traffic predictions to address service overprovisioning and improve adaptability to traffic changes, while ensuring the necessary quality-of-service (QoS) levels. An encoder-decoder deep learning model is initially leveraged for multi-step ahead prediction by analyzing real-traffic traces. This information is then exploited by multi-period planning heuristics to efficiently utilize available network resources while minimizing undesired service disruptions (caused due to lightpath re-allocations), with these heuristics outperforming a single-step ahead prediction approach. Network capacity demand is rapidly increasing, due to the emergence of new services and applications. To cope with this growing demand, the use of machine learning (ML) techniques for traffic-driven service provisioning has emerged as a promising solution to effectively model real-world traffic traces [1] and deal with overprovisioning that is present in staticallyprovisioned elastic optical networks (EONs) [2].