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  2. Linear trend estimation - Wikipedia

    en.wikipedia.org/wiki/Linear_trend_estimation

    Data patterns, or trends, occur when the information gathered tends to increase or decrease over time or is influenced by changes in an external factor. Linear trend estimation essentially creates a straight line on a graph of data that models the general direction that the data is heading.

  3. Trend analysis - Wikipedia

    en.wikipedia.org/wiki/Trend_analysis

    If the trend can be assumed to be linear, trend analysis can be undertaken within a formal regression analysis, as described in Trend estimation. If the trends have other shapes than linear, trend testing can be done by non-parametric methods, e.g. Mann-Kendall test, which is a version of Kendall rank correlation coefficient.

  4. Seasonal adjustment - Wikipedia

    en.wikipedia.org/wiki/Seasonal_adjustment

    Seasonal adjustment or deseasonalization is a statistical method for removing the seasonal component of a time series.It is usually done when wanting to analyse the trend, and cyclical deviations from trend, of a time series independently of the seasonal components.

  5. Marketing mix modeling - Wikipedia

    en.wikipedia.org/wiki/Marketing_mix_modeling

    Marketing mix modeling (MMM) is an analytical approach that uses historic information to quantify impact of marketing activities on sales. Example information that can be used are syndicated point-of-sale data (aggregated collection of product retail sales activity across a chosen set of parameters, like category of product or geographic market) and companies’ internal data.

  6. Moving average - Wikipedia

    en.wikipedia.org/wiki/Moving_average

    The Moving Median is a more robust alternative to the Moving Average when it comes to estimating the underlying trend in a time series. While the Moving Average is optimal for recovering the trend if the fluctuations around the trend are normally distributed, it is susceptible to the impact of rare events such as rapid shocks or anomalies.

  7. 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]

  8. Industry average - Wikipedia

    en.wikipedia.org/wiki/Industry_average

    It is a technical analysis based on historical data to estimate the trend of the data thus forecasting for its futures. [13] Comparing financial ratios over periods of time to determine financial performance of the business. Comparing current with past figures to examine the trending, whether it's getting better or deteriorating over time.

  9. Tracking signal - Wikipedia

    en.wikipedia.org/wiki/Tracking_signal

    Forecasts can relate to sales, inventory, or anything pertaining to an organization's future demand. The tracking signal is a simple indicator that forecast bias is present in the forecast model. It is most often used when the validity of the forecasting model might be in doubt.