Forecasting time series with multiple levels of seasonality

Abstract

Complex multiple seasonality is an important emerging challenge in time series forecasting In this first part of this thesis we propose models under a framework to forecast such time series The framework segregates the task into two stages In the first stage the time series is aggregated and existing time series models such as regression Box Jenkins or TBATS are used to fit this lower frequency data In the second stage additive or multiplicative seasonality at the higher frequency levels may be estimated using classical or function based methods Finally the estimates from the two stages are combined The proposed framework is capable of handling covariates is low on computational complexity and does not suffer from instability in forecasting for example overfitting We compare the forecast accuracy of this framework with a popular method proposed by De Livera et al 2011 and show that the proposed framework performs favourably in both short day ahead forecast and long term forecast horizons one year ahead forecast in a few case studies newline newline