Catatan bukti metode

Time-Series Cross-Validation

Time-series cross-validation is a resampling procedure designed for sequentially ordered data. Instead of randomly partitioning observations — which would destroy temporal structure and introduce data leakage — it advances a forecast origin one step at a time, fitting a model on all past data up to that origin and evaluating it on the immediately following out-of-sample period. Economists, financial analysts, and meteorologists use it whenever an honest, operationally realistic estimate of predictive accuracy is required for a time-ordered process.

Sources recorded, not reviewed

Catatan sumber

Kutipan disalin apa adanya dari catatan sumber metode. Tidak ada verifikasi tingkat klaim yang disimpulkan darinya.

Time-Series Cross-Validation (Rolling/Expanding Window)

Catatan metode taksonomi · process-pipeline / econometrics

Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192–213. · DOI 10.1016/j.ins.2011.12.028

Buka metode lengkap

Klaim yang dikurasi

Klaim tersimpan dalam buku besar bukti, masing-masing dengan penilaiannya sendiri.

Belum ada klaim yang dikurasi

Tampilan ini tidak menciptakan penilaian klaim ketika buku besar tidak memilikinya.

Metode terkait

Dihasilkan dari grafik metode dan ditampilkan sebagai relasi yang disarankan mesin — tidak ada klaim bukti yang disimpulkan.

Used in the same domainARIMAmachine-suggested · Relational suggestion, not evidence.See alsoBootstrap Inferencemachine-suggested · Relational suggestion, not evidence.Used in the same domainDiebold-Mariano Testmachine-suggested · Relational suggestion, not evidence.