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An annual plant is a plant that completes its life cycle, from germination to the production of seeds, within one growing season, and then dies. The length of growing seasons and period in which they take place vary according to geographical location, and may not correspond to the four traditional seasonal divisions of the year. With respect to the traditional seasons, annual plants are generally categorized into summer annuals and winter annuals. Summer annuals germinate during spring or early summer and mature by autumn of the same year. Winter annuals germinate during the autumn and mature during the spring or summer of the following calendar year.
One seed-to-seed life cycle for an annual plant can occur in as little as a month in some species, though most last several months. Oilseed rapa can go from seed-to-seed in about five weeks under a bank of fluorescent lamps. This style of growing is often used in classrooms for education. Many desert annuals are therophytes, because their seed-to-seed life cycle is only weeks and they spend most of the year as seeds to survive dry conditions.
In statistics, a moving average (rolling average or running average) is a calculation to analyze data points by creating a series of averages of different selections of the full data set. It is also called a moving mean (MM) or rolling mean and is a type of finite impulse response filter. Variations include: simple, cumulative, or weighted forms (described below).
Given a series of numbers and a fixed subset size, the first element of the moving average is obtained by taking the average of the initial fixed subset of the number series. Then the subset is modified by "shifting forward"; that is, excluding the first number of the series and including the next value in the subset.
A moving average is commonly used with time series data to smooth out short-term fluctuations and highlight longer-term trends or cycles. The threshold between short-term and long-term depends on the application, and the parameters of the moving average will be set accordingly. It is also used in economics to examine gross domestic product, employment or other macroeconomic time series. Mathematically, a moving average is a type of convolution and so it can be viewed as an example of a low-pass filter used in signal processing. When used with non-time series data, a moving average filters higher frequency components without any specific connection to time, although typically some kind of ordering is implied. Viewed simplistically it can be regarded as smoothing the data.