Statistical inference for ARTFIMA time series with stable innovations

Farzad Sabzikar; Jinu Kabala

Statistical inference for ARTFIMA time series with stable innovations

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arxiv 2103.07717 v1 pith:FO5SP5ZY submitted 2021-03-13 stat.AP

Jinu Kabala , Farzad Sabzikar This is my paper · ORCID

classification stat.AP

keywords seriestimemodeltempereddependencefractionallyinnovationsintegrated

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Autoregressive tempered fractionally integrated moving average with stable innovations modifies the power-law kernel of the fractionally integrated time series model by adding an exponential tempering factor. The tempered time series is a stationary model that can exhibits semi-long-range dependence. This paper develops the basic theory of the tempered time series model, including dependence structure and parameter estimation.

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Comparative analysis of financial data differentiation techniques using LSTM neural network
q-fin.ST 2025-05 conditional novelty 4.0 of 10

Fractionally differenced price series, especially with a differencing order estimated from an ARFIMA model, improved LSTM forecasts and portfolio trading metrics compared to logarithmic returns.

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