Why does the abstract claim the Verhulst improved model is more precise than the GM(1,1) improved model for violent changing data series?
The abstract states that the GM(1,1) model has low precision for predicting violent non-stationary data series. To address this disadvantage, it proposes both a GM(1,1) improved model and a Verhulst improved model using metabolism methods. Based on an empirical comparison using traffic accident data from 1996 to 2009 in Heilongjiang Province, the abstract concludes that the Verhulst improved model is more precise than the GM(1,1) improved model specifically for violent changing data series.
Conditions
- The comparison applies to violent changing or violent non-stationary data series.
- The empirical evaluation uses traffic accident data from 1996 to 2009 of Heilongjiang Province.
Reasoning, step by step
- Identify the stated disadvantage of the baseline GM(1,1) model regarding violent non-stationary data series.
- Note the proposal of two improved models using metabolism methods.
- Observe the empirical comparison conducted on the specified regional dataset.
- Conclude that the Verhulst improved model outperforms the GM(1,1) improved model for this specific type of data series.
Example
The abstract explicitly states: "In view of the disadvantages of GM(1,1) model, which has low precision for prediction of violent non-stationary data series... Application of traffic accident data from 1996~2009 of Heilongjiang Province shows that the Verhulst improved model is more precise than GM(1,1) improved model for violent changing data series."
Common misconceptions
- Assuming the video derives the mathematical proof of this precision difference; it only displays the abstract's textual claim.
- Believing the 85% average precision applies to the GM(1,1) model; the abstract attributes it to the Verhulst improved model.
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Answers are generated from source material and independently checked. Consult the original video or creator if something is unclear.