Forecasting overview
Forecasting is a technique where you analyze historical data in order to make an informed prediction about future trends. For example, you might analyze historical sales data from several store locations in order to predict future sales at those locations. In BigQuery ML, you perform forecasting on time series data.
You can perform forecasting in the following ways:
- By using the
AI.FORECASTfunction with the built-in TimesFM model . Use this approach when you need to forecast future values for a single variable, and don't require the ability to fine-tune the model. This approach doesn't require you to create and manage a model. - By using the
ML.FORECASTfunction with theARIMA_PLUSmodel . Use this approach when you need to run an ARIMA-based modeling pipeline and decompose the time series into multiple components in order to explain the results. This approach requires you to create and manage a model. - By using the
ML.FORECASTfunction with theARIMA_PLUS_XREGmodel . Use this approach when you need to forecast future values for multiple variables. This approach requires you to create and manage a model.
ARIMA_PLUS
and ARIMA_PLUS_XREG
time series models aren't actually single
models, but rather a time series modeling pipeline that includes multiple
models and algorithms. For more information, see Time series modeling pipeline
.
In addition to forecasting, you can use ARIMA_PLUS
and ARIMA_PLUS_XREG
models for anomaly detection. For more information, see the following
documents:
- Anomaly detection overview
- Perform anomaly detection with a multivariate time-series forecasting model
You can use the AI.EVALUATE
function
to evaluate forecasted data against actual values from historical data.
Compare the TimesFM and ARIMA
models
Use the following table to determine whether to use AI.FORECAST
with the
built-in TimesFM model or ML.FORECAST
with an ARIMA_PLUS
or ARIMA_PLUS_XREG
model for your use case:
AI.FORECAST
with a TimesFM modelML.FORECAST
with an ARIMA_PLUS
or ARIMA_PLUS_XREG
modelARIMA
algorithm for the
trend component, and a variety of other algorithms for non-trend
components. For more information, see Time series modeling pipeline
.ARIMA_PLUS
or ARIMA_PLUS_XREG
model is trained for each time series.CREATE MODEL
statement and a
function call.CREATE MODEL
statement
offers arguments that let you tune many model settings, such as the
following: - Seasonality
- Holiday effects
- Step changes
- Trend
- Spikes and dips removal
- Forecasting upper and lower bounds
- Quick forecasts
- Need minimal setup
- Model needs fine tuning
- Need explainability for model output
- Model input needs more context
Recommended knowledge
By using the default settings of BigQuery ML's statements and functions, you can create and use a forecasting model even without much ML knowledge. However, having basic knowledge about ML development, and forecasting models in particular, helps you optimize both your data and your model to deliver better results. We recommend using the following resources to develop familiarity with ML techniques and processes:
