2018 Fiscal Year Final Research Report
Multivariate time series modeling with sparse regularization and its applications
| Project/Area Number |
16K00067
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Keywords | スパース正則化 / 円滑閾値型推定方程式 / ボラティリティ / 経験類似度 / トピックモデル / 多変量自己回帰モデル / 対数死亡率 / マスク効果 |
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| Outline of Final Research Achievements |
We promoted the smooth-threshold estimation equations (STEE) to develop a prediction model with high accuracy even in high dimensional time series analysis. First, we worked with variable selection problem in volatility forecasting. We focused on empirical similarity-based models which turned out to produce better forecasting. We also compared topic score series which were extracted news text data using a dynamic topic model. Some topic score series are found to help forecasting. We also applied sparse regularization to vector autoregressive models, especially to the residual vector series from Lee-Carter model for log-mortality. Finally we proposed a variable selection method with which we can salvage true causal variables masked by other variables with strong marginal correlation.
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| Free Research Field |
統計科学
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| Academic Significance and Societal Importance of the Research Achievements |
IoTの推進により，学術・社会の両面でさまざまなセンサーデータが取得可能になっており，その多くは時間と共に観測される時系列データで，往々にして多変量である．従来の多変量時系列モデルは，比較的少数の変数間の相互共分散を通じてリード・ラグ関係を抽出するものであったが，ラグが深くなると高次元では推定が破綻する．本研究で試みたスパース推定との組合せは，今後の大容量の時系列解析につながる成果である．
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