机构:
Univ Catania, Dept Phys & Astron, I-95123 Catania, Italy
Ist Nazl Fis Nucl, I-95123 Catania, Italy
Museo Stor Fis, I-00184 Rome, Italy
Ctr Studi & Ric Enrico Fermi, I-00184 Rome, ItalyUniv Catania, Dept Phys & Astron, I-95123 Catania, Italy
La Rocca, Paola
[1
,2
,3
,4
]
Riggi, Daniele
论文数: 0引用数: 0
h-index: 0
机构:
Univ Milano Bicocca, Dept Stat, I-20126 Milan, ItalyUniv Catania, Dept Phys & Astron, I-95123 Catania, Italy
Riggi, Daniele
[5
]
Riggi, Francesco
论文数: 0引用数: 0
h-index: 0
机构:
Univ Catania, Dept Phys & Astron, I-95123 Catania, Italy
Ist Nazl Fis Nucl, I-95123 Catania, ItalyUniv Catania, Dept Phys & Astron, I-95123 Catania, Italy
Riggi, Francesco
[1
,2
]
机构:
[1] Univ Catania, Dept Phys & Astron, I-95123 Catania, Italy
[2] Ist Nazl Fis Nucl, I-95123 Catania, Italy
[3] Museo Stor Fis, I-00184 Rome, Italy
[4] Ctr Studi & Ric Enrico Fermi, I-00184 Rome, Italy
[5] Univ Milano Bicocca, Dept Stat, I-20126 Milan, Italy
Time series of atmospheric pressure data, collected over a period of several years, were analysed to provide undergraduate students with educational examples of application of simple statistical methods of analysis. In addition to basic methods for the analysis of periodicities, a comparison of two forecast models, one based on autoregression algorithms, and the other making use of an artificial neural network, was made. Results show that the application of artificial neural networks may give slightly better results compared to traditional methods.