Forecasting Using Information and Entropy Based on Belief Functions
Table 6
Regression results.
norm
std
sstd
Shannon entropy
Renyi entropy
Tsallis entropy
Estimate
Intercept
−43.7037 (0.0000) [0.0000]
−43.3522 (0.0000) [0.0000]
−41.3097 (0.0000) [0.0000]
−38.6949 (0.0000)
−38.6950 (0.0000)
−38.7344 (0.0000)
AIR
0.8891 (0.0000) [0.0000]
0.8980 (0.0000) [0.0000]
0.8605 (0.0000) [0.0000]
0.7894 (0.0000)
0.7890 (0.0000)
0.7317 (0.0000)
WATER
0.8166 (0.0049) [0.0723]
0.7823 (0.0043) [0.1001]
0.8154 (0.0079) [0.0000]
1.1918 (0.0007)
1.1918 (0.0007)
1.2315 (0.0008)
ACID
−0.1071 (0.3361) [0.2179]
−0.1094 (0.3114) [0.2357]
−0.1155 (0.1733) [0.2495]
−0.1821 (0.2631)
−0.1821 (0.2630)
−0.1828 (0.2644)
() is value, [] is , and ∗∗∗ is . For the case of entropy, the support is initially set to (−50, 0, 50) and the supports for to (−3, 0, 3), where is computed form the conventional LS estimation.
