Components of time series
A time series is a series of data points, typically consisting of successive measurements made over a time interval. A time series is often a sequence taken at successive equally spaced points in time. The four components of a time series are the data points, the time interval, the level, and the trend.
A time series is a collection of data points, typically consisting of successive measurements of a variable at equally spaced time intervals. Most commonly, a time series is a sequence taken at successive equally spaced points in time. This type of series is called an Uniform Time Series. However, not all time series are uniform. For example, monthly sales data will have 12 data points for each year; one for each month of the year. In this type of series the measurements are not equally spaced in time; they occur at irregular intervals. Such series are called Non-uniform or Irregular Time Series.
Components of Time Series:
A typical time series can be decomposed into four components:
Seasonal: Seasonality occurs when a pattern exists within quarterly or yearly observations repeating with or without change every period.
Cyclic: Cyclicity occurs when patterns repeat but do not necessarily do so at integer multiples of the length of the cycle.
Trend: A trend exists when there is a long-term increase or decrease in the data. It does not have to be linear. These fluctuations may be due to trends in the economy, business conditions, environmental conditions (e.g., droughts), etc.
Irregular or Random: The irregular component is also referred to as the residuals, error terms or noise component. This component represents effects that cannot be attributed to any of the other three components and that appear to be randomly distributed over time.”
A cyclical component is a pattern in data that repeats but does not trend. This means that if the pattern is graphed, it would appear as a wave. Cyclical components are usually caused by seasonal changes, such as changes in temperature or demand.
One of the most important components of time series is trend. Trend indicates the general direction in which the data is moving. It can be linear or non-linear, increasing or decreasing, and can contain seasonal influences. Most economic time series contain a significant trend component. The trend component can be removed from a time series to help identify other features such as seasonality or cycles.
A time series is a collection of data points, typically consisting of successive measurements made over a time interval. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data.
Time series data can be decomposed into four components: trend, seasonality, cycles, and irregularity.
The trend is the long-term direction of the time series. It may be linear or nonlinear. The seasonality is the repeating short-term cyclical pattern of the series. The cycle is the repeating long-term pattern of the series. The irregularity (also known as noise or randomness) is the random variation in the time series that cannot be explained by the other components.