: Data is often broken down into four key components: Trend : The long-term increase or decrease in the data.
: Periodic fluctuations that occur at fixed intervals (e.g., higher sales every December). Introduction to Time Series and Forecasting
: A stationary time series has statistical properties (like mean and variance) that do not change over time, which is a common requirement for many forecasting models. : Data is often broken down into four
: Random noise or "leftover" variation after accounting for the other components. Common Forecasting Methods : Random noise or "leftover" variation after accounting
: This refers to the correlation of a signal with a delayed version of itself. It is a critical concept because current values often depend on past values.
: Ups and downs that are not of a fixed period, often related to business cycles.
Time series analysis and forecasting involve analyzing sequences of data points collected at consistent intervals—such as daily, monthly, or yearly—to predict future values. This technique is essential in fields like finance, weather forecasting, and supply chain management because it identifies patterns that are time-dependent, such as trends and cycles. Core Concepts of Time Series
