Forecasting Using Information and Entropy Based on Belief Functions
Table 7
Regression results of the reduced model with only two inputs.
norm
std
sstd
Shannon entropy
Renyi entropy
Tsallis entropy
Estimate
Intercept
−57.0752 (0.0000) [0.0000]
−43.8121 (0.0000) [0.0000]
−32.0274 (0.0000) [0.0000]
−51.8325 (0.0000)
−41.4330 (0.0000)
−50.9705 (0.0000)
AIR
0.8629 (0.0000) [0.0000]
0.8354 (0.0000) [0.0000]
0.5923 (0.0000) [0.0000]
0.7633 (0.0000)
0.7561 (0.0000)
0.7029 (0.0000)
WATER
0.8033 (0.0233) [0.0000]
0.5056 (0.0709) [0.0000]
0.7831 (0.0079) [0.0000]
1.1479 (0.0000)
1.1479 (0.0000)
1.1358 (0.0000)
Expected prediction
26.5564 <19.1543, −32.4528>
27.0138 <8.3351, −32.3311>
24.5375 <18.4833, −32.1598>
23.5138 <17.9744, 34.1935>
26.1581 <18.1415, 34.8077>
24.0054 <17.0348, 34.5871>
Prediction bias
11.5564
12.0138
9.5375
8.5138
11.1581
9.0054
<> is the prediction interval, ( ) is value, [ ] is , and ∗∗∗ is . For the case of Entropy, the support is initially set to (−100, 0, 100) and the supports for to (−3, 0, 3), where is computed from the conventional LS estimation.
