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Jurnal

International Journal of Forecasting

Scopus

ISSN 0169-2070 · Elsevier BV · 7 artikel di indeks

  1. Artikel Jurnal Teks lengkap

    Forecasting: theory and practice

    Fotios Petropoulos; Daniele Apiletti; Vassilios Assimakopoulos dkk.

    International Journal of Forecasting · 2022 · Inggris · OpenAlex Scopus

    Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a…

  2. Artikel Jurnal Teks lengkap

    M5 accuracy competition: Results, findings, and conclusions

    Spyros Makridakis; Evangelos Spiliotis; Vassilios Assimakopoulos

    International Journal of Forecasting · 2022 · Inggris · OpenAlex Scopus

    In this study, we present the results of the M5 “Accuracy” competition, which was the first of two parallel challenges in the latest M competition with the aim of advancing the theory and practice of forecasting. The main objective in the M5 “Accuracy” competition was to accurately predict 42,840…

  3. Artikel Jurnal Teks lengkap

    Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

    Kin G. Olivares; Cristian Challú; Grzegorz Marcjasz dkk.

    International Journal of Forecasting · 2022 · Inggris · OpenAlex Scopus

    We extend neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well-performing deep learning model, extending its capabilities by including exogenous variables and allowing it to integrate multiple sources of useful…

  4. Artikel Jurnal Teks lengkap

    Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

    Bryan Y. Lim; Sercan Ö. Arık; Nicolas Loeff dkk.

    International Journal of Forecasting · 2021 · Inggris · OpenAlex Scopus

    Multi-horizon forecasting often contains a complex mix of inputs – including static (i.e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed in the past – without any prior information on how they interact with the target. Several deep learning…