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  2. Exponential smoothing - Wikipedia

    en.wikipedia.org/wiki/Exponential_smoothing

    Exponential smoothing or exponential moving average (EMA) is a rule of thumb technique for smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time. It is an easily learned ...

  3. 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 ...

  4. Forecasting - Wikipedia

    en.wikipedia.org/wiki/Forecasting

    In such situations, the forecasting procedure calculates the seasonal index of the "season" – seven seasons, one for each day – which is the ratio of the average demand of that season (which is calculated by Moving Average or Exponential Smoothing using historical data corresponding only to that season) to the average demand across all seasons.

  5. Moving average - Wikipedia

    en.wikipedia.org/wiki/Moving_average

    Smoothing of a noisy sine (blue curve) with a moving average (red curve). In statistics, a moving average (rolling average or running average or moving mean [1] or rolling mean) is a calculation to analyze data points by creating a series of averages of different selections of the full data set.

  6. Autoregressive integrated moving average - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_integrated...

    The default Expert Modeler feature evaluates a range of seasonal and non-seasonal autoregressive (p), integrated (d), and moving average (q) settings and seven exponential smoothing models. The Expert Modeler can also transform the target time-series data into its square root or natural log.

  7. 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.

  8. Smoothing - Wikipedia

    en.wikipedia.org/wiki/Smoothing

    Smoothing may be distinguished from the related and partially overlapping concept of curve fitting in the following ways: . curve fitting often involves the use of an explicit function form for the result, whereas the immediate results from smoothing are the "smoothed" values with no later use made of a functional form if there is one;

  9. Demand forecasting - Wikipedia

    en.wikipedia.org/wiki/Demand_forecasting

    Demand forecasting plays an important role for businesses in different industries, particularly with regard to mitigating the risks associated with particular business activities. However, demand forecasting is known to be a challenging task for businesses due to the intricacies of analysis, specifically quantitative analysis. [4]