2017年11月28日 · Consider a forecast process which is designed to create unconstrained end-customer demand forecast. This means that the forecast generation process does not consider supply or distribution constraints. When we measure the effectiveness of this process, the forecast may have both bias and inaccuracy (measured as MAPE, e.g.)

2019年7月17日 · I am working on a financial model that will forecast the revenue a company generates over a fiscal quarter, and I am not sure of the best way to rigorously evaluate the bias in the model. Every day throughout the quarter I generate a new forecast for what revenue will be at the end of the quarter.

2016年11月28日 · What is the impact of Large Forecast Errors? Is Negative accuracy meaningful? Regardless of huge errors, and errors much higher than 100% of the Actuals or Forecast, we interpret accuracy a number between 0% and 100%. Either a forecast is perfect or relative accurate or inaccurate or just plain incorrect.

2016年11月24日 · I use point forecast accuracy measures that reward unbiased forecasts, like the MSE or scaled versions if I want to compare forecasts on different scales (unless I can reasonably expect future realizations to be symmetrically distributed). I always look at bias, not only at accuracy.

2018年7月18日 · I have calculated BIAs and MPE for a forecasted decompositional model, BIAS comes out as underpredicting and MPE comes out as overpredicting. Is this possible seen as they both measure how biased a

2019年7月8日 · Optimization on RMSE yields an average-based number... whereas on MAE yields a median-based forecast. Forecast KPI: RMSE, MAE, MAPE & Bias. Advantages of using median forecast: robust to outliers. Disadvantages of using median forecast: bad for intermittent time series data, medians can be biased for non-normal data, median forecasts are not ...

2021年9月28日 · How can we define forecast unbiasedness, e.g. in terms of expectations? Is there some textbook reference? If the estimated model is not linear regression, but e.g. GLM, or random forest, is the forecast unbiasedness defined the same way as for linear regression? If possible, use the notation I introduced.

2022年5月24日 · So even if the D-4 forecast in the future is perfect, the impact will still be biased. It seems to me like it will introduce 2 biases, the "relationship" bias in the past + the possible forecast bias in the future (both will not always cancel each other) Plus, using option (1) ensures that the more D-4 becomes accurate the better becomes the ...

2017年8月25日 · $\begingroup$ @Ben: in that case, we won't divide by zero. However, the asymmetry is still a slight problem. If your forecast is 293K and the actual is 288K, you have an APE of 1.74%, and if the forecast is 288K while the actual is 293K, the APE is 1.71%, so the second forecast looks better, though both are off by 5K.

Dampening can be thought of as a special case of shrinkage methods; these methods as a whole tend to reduce uncertainty in estimates (yet another circumstance of trading bias for variance, an ever-recurring theme in statistics, though in some cases, such as many involving variable-selection, shrinkage can reduce both bias and variance).