Forecasting highly persistent time series with bounded spectrum processes

Federico Maddanu ()
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Federico Maddanu: CY University

Statistical Papers, 2023, vol. 64, issue 1, No 13, 285-319

Abstract: Abstract Long memory models can be generalised by the Fractional equal-root Autoregressive Moving Average (FerARMA) process, which displays short memory for a suitable parameter’s set. Consequently, the spectrum is bounded, ensuring stationarity also for values of the memory parameter d larger than 0.5. The FerARMA generalization is proposed here to forecast highly persistent time series, as climate records of tree rings and paleo-temperature reconstructions. The main advantage of a bounded spectrum allows for more accurate predictions with respect to standard long memory models, especially if a long prediction horizon is considered.

Keywords: Fractional processes; Long memory; Non stationarity; Bounded spectrum; Forecasting; Climate time series; 60G10; 62M10 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00362-022-01321-z

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