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  2. Mean absolute scaled error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_scaled_error

    Asymptotic normality of the MASE: The Diebold-Mariano test for one-step forecasts is used to test the statistical significance of the difference between two sets of forecasts. [ 5 ] [ 6 ] [ 7 ] To perform hypothesis testing with the Diebold-Mariano test statistic, it is desirable for D M ∼ N ( 0 , 1 ) {\displaystyle DM\sim N(0,1)} , where D M ...

  3. Forecasting - Wikipedia

    en.wikipedia.org/wiki/Forecasting

    Further, two-step-ahead or in general p-step-ahead forecasts can be computed by first forecasting the value immediately after the training set, then using this value with the training set values to forecast two periods ahead, etc. See also. Calculating demand forecast accuracy; Consensus forecasts; Forecast error; Predictability

  4. Forecast error - Wikipedia

    en.wikipedia.org/wiki/Forecast_error

    If a main application of the forecast is to predict when certain thresholds will be crossed, one possible way of assessing the forecast is to use the timing-error—the difference in time between when the outcome crosses the threshold and when the forecast does so.

  5. Mean squared prediction error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_prediction_error

    When the model has been estimated over all available data with none held back, the MSPE of the model over the entire population of mostly unobserved data can be estimated as follows.

  6. Prediction interval - Wikipedia

    en.wikipedia.org/wiki/Prediction_interval

    For example, to calculate the 95% prediction interval for a normal distribution with a mean (μ) of 5 and a standard deviation (σ) of 1, then z is approximately 2. Therefore, the lower limit of the prediction interval is approximately 5 ‒ (2⋅1) = 3, and the upper limit is approximately 5 + (2⋅1) = 7, thus giving a prediction interval of ...

  7. Mean directional accuracy - Wikipedia

    en.wikipedia.org/wiki/Mean_Directional_Accuracy

    While accuracy, as measured by quantitative errors, is important, it may be more crucial to accurately forecast the direction of change. Directional accuracy is similar to a binary evaluation. The metric only considers the upward or downward direction in the time series and is independent of quantitive value of increase or decrease.

  8. X-13ARIMA-SEATS - Wikipedia

    en.wikipedia.org/wiki/X-13ARIMA-SEATS

    Calculate another estimate of the trend using a different set of weights (known as "Henderson weights"). Remove the trend again and calculate another estimate of the seasonal factor. Seasonally adjust the series again with the new seasonal factors. Calculate the final trend and irregular components from the seasonally adjusted series.

  9. Variance decomposition of forecast errors - Wikipedia

    en.wikipedia.org/wiki/Variance_decomposition_of...

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