Prophet Forecasting

• InfluxDB 3 Core/Enterprise: with the Processing Engine enabled.
• Python packages:
  • pandas (for data manipulation)
  • numpy (for numerical operations)
  • requests (for HTTP requests)
  • prophet (for time series forecasting)
• Notification Sender Plugin (optional): Required if using the senders parameter. See the influxdata/notifier plugin.

Installation steps

1. Start InfluxDB 3 with the Processing Engine enabled (--plugin-dir /path/to/plugins):

influxdb3 serve \
  --node-id node0 \
  --object-store file \
  --data-dir ~/.influxdb3 \
  --plugin-dir ~/.plugins

1. Install required Python packages:

influxdb3 install package pandas
influxdb3 install package numpy
influxdb3 install package requests
influxdb3 install package prophet

1. (Optional) For notifications, install the influxdata/notifier plugin and create an HTTP trigger for it.

Plugin parameters may be specified as key-value pairs in the --trigger-arguments flag (CLI) or in the trigger_arguments field (API) when creating a trigger. Some plugins support TOML configuration files, which can be specified using the plugin’s config_file_path parameter.

If a plugin supports multiple trigger specifications, some parameters may depend on the trigger specification that you use.

Plugin metadata

This plugin includes a JSON metadata schema in its docstring that defines supported trigger types and configuration parameters. This metadata enables the InfluxDB 3 Explorer UI to display and configure the plugin.

Scheduled trigger parameters

Set these parameters with --trigger-arguments when creating a scheduled trigger:

HTTP request parameters

Send these parameters as JSON in the HTTP POST request body:

Advanced parameters

Notification parameters

TOML configuration

To use a TOML configuration file, set the PLUGIN_DIR environment variable and specify the config_file_path in the trigger arguments. This is in addition to the --plugin-dir flag when starting InfluxDB 3.

Example TOML configuration

prophet_forecasting_scheduler.toml

For more information on using TOML configuration files, see the Using TOML Configuration Files section in the influxdb3_plugins/README.md.

Scheduled trigger

Create a trigger for periodic forecasting:

influxdb3 create trigger \
  --database mydb \
  --path "gh:influxdata/prophet_forecasting/prophet_forecasting.py" \
  --trigger-spec "every:1d" \
  --trigger-arguments "measurement=temperature,field=value,window=30d,forecast_horizont=2d,tag_values=region:us-west.device:sensor1,target_measurement=temperature_forecast,model_mode=train,unique_suffix=20250619_v1" \
  prophet_forecast_trigger

HTTP trigger

Create a trigger for on-demand forecasting:

influxdb3 create trigger \
  --database mydb \
  --path "gh:influxdata/prophet_forecasting/prophet_forecasting.py" \
  --trigger-spec "request:forecast" \
  prophet_forecast_http_trigger

Enable triggers

influxdb3 enable trigger --database mydb prophet_forecast_trigger
influxdb3 enable trigger --database mydb prophet_forecast_http_trigger

Example 1: Basic scheduled forecasting

Write historical data and create a forecast:

# Write historical temperature data
influxdb3 write \
  --database mydb \
  "temperature,region=us-west,device=sensor1 value=22.5"

# Create and enable the trigger
influxdb3 create trigger \
  --database mydb \
  --path "gh:influxdata/prophet_forecasting/prophet_forecasting.py" \
  --trigger-spec "every:1d" \
  --trigger-arguments "measurement=temperature,field=value,window=30d,forecast_horizont=2d,tag_values=region:us-west.device:sensor1,target_measurement=temperature_forecast,model_mode=train,unique_suffix=v1" \
  prophet_forecast

influxdb3 enable trigger --database mydb prophet_forecast

# Query forecast results (after trigger runs)
influxdb3 query \
  --database mydb \
  "SELECT time, forecast, yhat_lower, yhat_upper FROM temperature_forecast ORDER BY time DESC LIMIT 5"

Expected output

+----------------------+---------+------------+------------+
| time                 | forecast| yhat_lower | yhat_upper |
+----------------------+---------+------------+------------+
| 2025-06-21T00:00:00Z | 23.2    | 21.8       | 24.6       |
| 2025-06-20T00:00:00Z | 22.9    | 21.5       | 24.3       |
+----------------------+---------+------------+------------+

Example 2: On-demand HTTP forecasting

Example HTTP request for on-demand forecasting:

curl -X POST http://localhost:8181/api/v3/engine/forecast \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "measurement": "temperature",
    "field": "value",
    "forecast_horizont": "7d",
    "tag_values": {"region":"us-west","device":"sensor1"},
    "target_measurement": "temperature_forecast",
    "unique_suffix": "model_v1_20250722",
    "start_time": "2025-05-20T00:00:00Z",
    "end_time": "2025-06-19T00:00:00Z",
    "seasonality_mode": "additive",
    "changepoint_prior_scale": 0.05,
    "validation_window": "3d",
    "msre_threshold": 0.05
  }'

Advanced forecasting with holidays

curl -X POST http://localhost:8181/api/v3/engine/forecast \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "measurement": "sales",
    "field": "revenue",
    "forecast_horizont": "30d",
    "tag_values": {"store":"main_branch"},
    "target_measurement": "revenue_forecast",
    "unique_suffix": "retail_model_v2",
    "start_time": "2024-01-01T00:00:00Z",
    "end_time": "2025-06-01T00:00:00Z",
    "holiday_country_names": ["US"],
    "holiday_date_list": ["2025-07-04"],
    "holiday_names": ["Independence Day"],
    "changepoints": ["2025-01-01", "2025-03-01"],
    "inferred_freq": "1D"
  }'

Files

• prophet_forecasting.py: The main plugin code containing handlers for scheduled and HTTP triggers
• prophet_forecasting_scheduler.toml: Example TOML configuration file for scheduled triggers

Logging

Logs are stored in the trigger’s database in the system.processing_engine_logs table. To view logs:

influxdb3 query --database YOUR_DATABASE "SELECT * FROM system.processing_engine_logs WHERE trigger_name = 'prophet_forecast_trigger'"

Main functions

process_scheduled_call(influxdb3_local, call_time, args)

Handles scheduled forecasting tasks. Queries historical data, trains or loads Prophet model, generates forecasts, and writes results.

Key operations:

1. Parses configuration from arguments or TOML file
2. Queries historical data within specified window
3. Trains Prophet model or loads existing model
4. Generates forecasts for specified horizon
5. Optionally validates against actual data and sends alerts

process_http_request(influxdb3_local, request_body, args)

Handles on-demand forecast requests via HTTP. Supports backfill operations with configurable time ranges.

Common issues

Issue: Model training failures

Solution: Ensure sufficient historical data points for the specified window. Verify data contains required time column and forecast field. Check for data gaps that might affect frequency inference. Set inferred_freq manually if automatic detection fails.

Issue: Validation failures

Solution: Review MSRE threshold settings - values too low may cause frequent failures. Ensure validation window provides sufficient data for comparison. Check that validation data aligns temporally with forecast period.

Issue: HTTP trigger issues

Solution: Verify JSON request body format matches expected schema. Check authentication tokens and database permissions. Ensure start_time and end_time are in valid ISO 8601 format with timezone.

Issue: Model persistence problems

Solution: Verify plugin directory permissions for model storage. Check disk space availability in plugin directory. Ensure unique_suffix values don’t conflict between different model versions.

Model storage

• Location: Models stored in prophet_models/ directory within plugin directory
• Naming: Files named prophet_model_{unique_suffix}.json
• Versioning: Use descriptive unique_suffix values for model management

Time format support

Supported time units for window, forecast_horizont, and validation_window:

• s (seconds), min (minutes), h (hours)
• d (days), w (weeks)
• m (months ≈30.42 days), q (quarters ≈91.25 days), y (years = 365 days)

Validation process

When validation_window is set:

1. Training data: current_time - window to current_time - validation_window
2. Validation data: current_time - validation_window to current_time
3. MSRE calculation: mean((actual - predicted)² / actual²)
4. Threshold comparison and optional alert dispatch

Output data structure

Forecast results are written to the target measurement with the following structure:

Tags

• model_version: Model identifier from unique_suffix parameter
• Additional tags from original measurement query filters

Fields

• forecast: Predicted value (yhat from Prophet model)
• yhat_lower: Lower bound of confidence interval
• yhat_upper: Upper bound of confidence interval
• run_time: Forecast execution timestamp (ISO 8601 format)

Timestamp

• time: Forecast timestamp in nanoseconds