Hybrid Statistical and AI Methods for Synthetic Time Series Generation in Practical Applications

Time series are one of the most common data types in data mining, capturing sequential observations over long periods. Creating synthetic time series data involves generating a sequence of data points that maintain the statistical properties found in real-world datasets. This article addresses the generation of synthetic time series data from real univariate time series datasets. It presents two novel methodologies grounded in Seasonal-Trend decomposition using LOESS (STL). The first method involves estimating the probability distribution of the residual component and generating new residuals to substitute in the STL decomposition. The second method replaces the noise component with a randomized version and synthesizes seasonality using Long Short-Term Memory (LSTM) neural networks. We applied these methodologies to time series data representing the operating times of household appliances. The synthetic data produced were assessed both quantitatively and qualitatively using a range of statistical indicators. Moreover, we improved the classification performance on imbalanced real data with synthetic data.