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  2. Trend analysis - Wikipedia

    en.wikipedia.org/wiki/Trend_analysis

    Trend analysis is the widespread practice of collecting information and attempting to spot a pattern. In some fields of study, the term has more formally defined meanings. In some fields of study, the term has more formally defined meanings.

  3. Mathematics - Wikipedia

    en.wikipedia.org/wiki/Mathematics

    Examples of unexpected applications of mathematical theories can be found in many areas of mathematics. A notable example is the prime factorization of natural numbers that was discovered more than 2,000 years before its common use for secure internet communications through the RSA cryptosystem. [127]

  4. Decomposition of time series - Wikipedia

    en.wikipedia.org/wiki/Decomposition_of_time_series

    The trend-cycle component can just be referred to as the "trend" component, even though it may contain cyclical behavior. [3] For example, a seasonal decomposition of time series by Loess (STL) [ 4 ] plot decomposes a time series into seasonal, trend and irregular components using loess and plots the components separately, whereby the cyclical ...

  5. Linear trend estimation - Wikipedia

    en.wikipedia.org/wiki/Linear_trend_estimation

    The mathematics of linear trend estimation is a variant of the standard ANOVA, giving different information, and would be the most appropriate test if the researchers hypothesize a trend effect in their test statistic. One example is levels of serum trypsin in six groups of subjects ordered by age decade (10–19 years up to 60–69 years ...

  6. Time series - Wikipedia

    en.wikipedia.org/wiki/Time_series

    Time series: random data plus trend, with best-fit line and different applied filters. In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time.

  7. Trend-stationary process - Wikipedia

    en.wikipedia.org/wiki/Trend-stationary_process

    In both unit root and trend-stationary processes, the mean can be growing or decreasing over time; however, in the presence of a shock, trend-stationary processes are mean-reverting (i.e. transitory, the time series will converge again towards the growing mean, which was not affected by the shock) while unit-root processes have a permanent ...