Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
ARIMA_PLUS BigQuery ML adalah model perkiraan univariat. Sebagai
model statistik, model ini lebih cepat dilatih daripada model yang berdasar pada jaringan neural.
Sebaiknya latih model ARIMA_PLUS BigQuery ML jika Anda perlu melakukan banyak iterasi cepat pada pelatihan model atau jika Anda memerlukan baseline yang murah untuk mengukur model lain.
Seperti Prophet,
ARIMA_PLUS BigQuery ML berupaya mengurai setiap deret waktu ke dalam
tren, musim, dan hari libur, sehingga menghasilkan perkiraan menggunakan gabungan
inferensi model ini. Namun, perbedaan pentingnya adalah
ARIMA+ BQML menggunakan ARIMA untuk membuat model komponen tren, sementara Prophet mencoba
menyesuaikan kurva menggunakan model linear atau piecewise logistic.
Google Cloud menawarkan pipeline untuk melatih model ARIMA_PLUS BigQuery ML
dan pipeline untuk mendapatkan inferensi batch dari model ARIMA_PLUS BigQuery ML.
Kedua pipeline tersebut merupakan instance
Vertex AI Pipelines dari
Komponen Pipeline (GCPC).Google Cloud
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-25 UTC."],[],[],null,["# Forecasting with ARIMA+\n\n| To see an example of how to train a model with ARIMA+,\n| run the \"Train a BigQuery ML ARIMA_PLUS model using Vertex AI tabular workflows\" notebook in one of the following\n| environments:\n|\n| [Open in Colab](https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/bqml_arima_plus.ipynb)\n|\n|\n| \\|\n|\n| [Open in Colab Enterprise](https://console.cloud.google.com/vertex-ai/colab/import/https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ftabular_workflows%2Fbqml_arima_plus.ipynb)\n|\n|\n| \\|\n|\n| [Open\n| in Vertex AI Workbench](https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ftabular_workflows%2Fbqml_arima_plus.ipynb)\n|\n|\n| \\|\n|\n| [View on GitHub](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/bqml_arima_plus.ipynb)\n\n\n[BigQuery ML ARIMA_PLUS](/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series) is a univariate forecasting model. As\na statistical model, it is faster to train than a [model based on neural networks](/vertex-ai/docs/tabular-data/forecasting/overview).\nWe recommend training a BigQuery ML ARIMA_PLUS model if you need to\nperform many quick iterations of model training or if you need an inexpensive\nbaseline to measure other models against.\n\nLike [Prophet](/vertex-ai/docs/tabular-data/forecasting-prophet),\nBigQuery ML ARIMA_PLUS attempts to decompose each time series into\ntrends, seasons, and holidays, producing a forecast using the aggregation of\nthese models' inferences. One of the many differences, however, is that\nBQML ARIMA+ uses ARIMA to model the trend component, while Prophet attempts to\nfit a curve using a piecewise logistic or linear model.\n\nGoogle Cloud offers a pipeline for training a BigQuery ML ARIMA_PLUS\nmodel and a pipeline for getting batch inferences from a BigQuery ML ARIMA_PLUS model.\nBoth pipelines are instances of\n[Vertex AI Pipelines](/vertex-ai/docs/pipelines/introduction) from\n[Google Cloud Pipeline Components](/vertex-ai/docs/pipelines/components-introduction) (GCPC).\n\nWhat's next\n-----------\n\n- Learn more about [BigQuery ML ARIMA_PLUS](/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series).\n- Learn about [the service accounts used by this workflow](/vertex-ai/docs/tabular-data/tabular-workflows/service-accounts#arima)."]]
