Applications of high-frequency data, including energy management, economics, and finance, frequently require time-series forecasting characterized by complex seasonality. Recognizing prevailing seasonal trends continues to be difficult, given that the majority of solutions depend on basic...

The classical X-11 seasonal adjustment method for monthly and quarterly time series is equipped with routines for data-driven selections of both Henderson trendcycle filters and 3 × k seasonal moving averages, currently involving up to three candidate filters in either case. Although these...

This paper examines the use of machine learning methods in modeling and forecasting time series with long memory through GARMA. By employing rigorous model selection criteria through simulation study, we find that the hybrid GARMA-LSTM model outperforms traditional approaches in forecasting...

Official statistics increasingly make use of higher-frequency time series. But when users ultimately are interested in a seasonally adjusted temporal aggregate of these data, we have to decide whether to perform seasonal adjustment or aggregation first. Consequently, we must weigh up the...

This paper evaluates the transition from X12-ARIMA to X13-ARIMA-SEATS for the seasonal adjustment of Armenia's quarterly national accounts (QNA). We analyze the methodological advancements and their impact on key economic indicators, focusing on the precision and reliability of seasonally...