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Time series data are used in a large variety of real-world applications. However, they often encounter the missing value problem due to data transmission errors, machine malfunction, or human errors. Existing imputation methods for missing values don't explicitly employ the temporal information embedded in the time series data. In this paper, we propose a new imputation method to fill up the missing...
In this paper, we apply the self-organizing multilayer perceptron (SOMLP) architecture proposed by Gas for temporal prediction. Our main idea is to divide a data series into several smaller sub-series which are treated as individual functions or signals. Then we can find the tendencies in detail and perform predictions based on the properties of these signals. By using the SOMLP, signals can be clustered...
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