| Project/Area Number |
26560163
|
|---|
| Research Category |
Grant-in-Aid for Challenging Exploratory Research
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Social systems engineering/Safety system
|
|---|
| Research Institution | The University of Tokyo |
|---|
Principal Investigator |
Ohnishi Takaaki 東京大学, 情報理工学(系)研究科, 准教授 (10376387)
|
|---|
| Project Period (FY) |
2014-04-01 – 2016-03-31
|
| Project Status |
Completed (Fiscal Year 2015)
|
| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2015: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2014: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
|
|---|
| Keywords | 経済物理学 / 超並列計算 / 不動産市場 / 価格予測 / 非線形 / k-近傍法 / 国際貿易ネットワーク / ネットワークモチーフ / スケールフリーネットワーク / 社会物理学 / k近傍法 / 機械学習 / データ駆動 |
|---|
| Outline of Final Research Achievements |
Dynamic and complex data observed in socio-economic phenomena are studied based on inductive approach. We proposed the use of k-nearest neighbor regression to automatically value real estate property. We showed that there is an optimal number of nearest neighbors minimizing the prediction error. As the number of explanation variables increases, the error becomes small. We estimated strength of the correlation and found its size and location are important to predict house price. We detected significant three-node motifs, which are significantly more abundant than expected by chance, observed in the world trade network of multiproduct world trade between countries. The obtained motifs characterize the country and reflect particular economic functions.
|
