Adaptive Normalization: A Novel Data Normalization Approach for Non-Stationary Time Series

Data normalization is a fundamental preprocessing step for mining and learning from data. However, finding an appropriated method to deal with time series normalization is not a simple task. This is because most of the traditional normalization methods make assumptions that do not hold for most time series. The first assumption is that all time series are stationary, i.e., their statistical properties, such as mean and standard deviation, do not change over time. The second assumption is that the volatility of the time series is considered uniform. None of the methods currently available in the literature address these issues. This paper proposes a new method for normalizing non-stationary heteroscedastic (with non-uniform volatility) time series. The method, named Adaptive Normalization (AN), was tested together with an Artificial Neural Network (ANN) in three forecast problems. The results were compared to other four traditional normalization methods, and showed AN improves ANN accuracy in both short-and long-term predictions.