We apply the ideas of singular value decomposition to time series to account for seasonal volatility. We propose a general method for producing reliable short-term point and interval forecasts of daily maximum tropospheric ozone concentrations, a time series with a significant seasonal component and correlated errors in both mean and volatility. Our method combines symmetrizing data transformation and time series modeling techniques called the singular spectrum analysis and autoregressive models.