Time series forecasting

Concept

Description

Job

A time-series forecasting job includes data features and algorithm model parameters.

Instance

A time-series forecasting job creates a time-series forecasting instance based on the configuration of the job. The instance pulls data at regular intervals, runs the algorithm model, and then distributes the analysis result based on the configuration of the job. For more information about the impacts of different operations on the scheduling and running of instances, see Scheduling and execution scenarios.

• Each job can create one or more instances. Only one instance can run in a job at a time regardless of whether the instance is running on schedule or is retried due to an anomaly. You cannot concurrently run multiple instances in a single job.

• You cannot modify the configuration of a job when the job is running. If you modify the configuration of a job, the job re-creates an instance to run algorithm models. The new instance is not related to the previous instance.

Instance ID

Each instance is identified by a unique ID.

Creation time

Each instance is created at a specific point in time. In most cases, an instance is created for a time-series forecasting job based on the scheduling rules of the job. If historical data needs to be processed or the delay caused by the timeout of the previous instance is offset, an instance is immediately created.

Start time

Each instance starts to run at a specific point in time. If the job to which an instance belongs is retried, the start time is the most recent time at which the instance starts to run.

End time

Each instance stops running at a specific point in time. If the job to which an instance belongs is retried, the end time is the most recent time at which the instance stops running.

Status

Each instance is in a specific state at a specific point in time. Valid values:

• RUNNING

• STARTING

• SUCCEEDED

• FAILED

Data feature configurations

Data feature configurations include time, entities, and features. For more information, see Create a time-series forecasting job.

Algorithm configurations

Algorithm configurations include cycle, holiday, and forecasting (including length of the sequence to be tested, confidence, number of samples, forecasting frequency, and observation duration). For more information, see Create a time-series forecasting job.

Forecasting results

Forecasting results are displayed in the built-in dashboard.

Scenario

Description

Scenario 1: Execute a time-series forecasting job from the current point in time

The time-series forecasting job reads historical data based on the configured observation length and then predicts the time series for a period of time in the future.

Scenario 2: Execute a time-series forecasting job from a historical point in time

If you configure a job to start at a previous point in time, algorithm models analyze data from the historical point in time to the current point in time.

Scenario 3: Modify the scheduling configurations

After you modify the scheduling configurations of a job, the job generates an instance based on the new configurations. Algorithm models record the point in time before which all historical data is analyzed and continue to analyze the most recent data.

Scenario 4: Retry a failed instance

If an instance fails to run due to issues such as insufficient permissions, unavailable source Logstore, unavailable destination Logstore, or invalid configurations, Simple Log Service can automatically retry to run the instance. If an instance is stuck in the STARTING state, the configuration of the instance may fail. Simple Log Service generates an error log record and sends the record to the internal-etl-log Logstore. You can verify the configuration of the instance and start the instance again. After the instance is scheduled and run, Simple Log Service changes the status of the instance to SUCCEEDED or FAILED based on the retry result.