Time-series patterns that repeat periodically after days or weeks are known as what?

Time-series patterns that repeat periodically after days or weeks are best described as seasonality. This phenomenon is characterized by fluctuations that occur at regular intervals, typically aligned with calendar cycles, such as daily, weekly, monthly, or yearly patterns. For example, retail sales may increase every holiday season or certain products may sell better during specific months of the year due to seasonal demand.

The concept of seasonality is crucial in supply chain and operations management because it helps businesses forecast demand more accurately and manage inventory levels accordingly. By recognizing these patterns, companies can optimize their operations and ensure they are prepared for predictable spikes or drops in customer demand.

In contrast, trends refer to long-term movements in data over time without a fixed period, cyclic patterns involve fluctuations that occur over longer intervals but are not strictly periodic like seasonality, and random variations reflect unpredictable changes that cannot be anticipated. Understanding these distinctions is essential for applying the right analytical techniques and strategies in supply chain management.