 OverviewWe are exploring
statistical methods for forecasting future behaviors of
time series. Recently information technologies and
optimization algorithms are aggressively applied to
various problems in the area of logistics and supply
chain management. Accurate forecasting of demand change
often improves the quality of solution to such resource
planning problems as production planning and
replenishment planning.
 Research items
- Time series data analysis
We analyze time series based on state
space model. Parameters in model and state
variables are estimated by maximum likelihood
method with numerical optimization or simulation.
- Huge time series data
Dealing with huge time series has a lot
of practical importance. We expect improvement of
accuracy of forecasting by aggregating several
analogous time series and distributing the
forecast at the root level to leaf level.
Therefore clustering of time series is a key
technology to deal with huge time series
effectively.
- Special time series data
State space model is very general, and
even Kalman filter, which is a linear and
Gaussian family of the model, can deal with
various cases. However, we need to design a
special model to achieve better accuracy of the
forecasting.
 Related information
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