Abstract:

As the applications for modeling of big data and analysis advance in scope, computational efficiency faces greater challenges in terms of storage and speed. In many practical problems, a great amount of historical data is sequentially collected and used for online statistical modeling. For modeling sequential data, we propose a sequential linear regression method that extracts essential information from historical data. This carefully selected information is then utilized to update a model according to a sequential estimation scheme. With this technique, the earlier data no longer needs to be stored, and the sequential updating is computationally efficient in speed and storage. A weighted strategy is introduced on the current model to determine the impact of data from different periods. When compared with estimation methods that use historical data, our numerical experiments demonstrate that our solution increases the speed while decreasing the storage load.

Key words: Big data ·, Attributive explanatory variables ·, Periodic spline ·Weighted least squares ·, Sequential estimation