Forecast error evaluator plugin

The Forecast Error Evaluator Plugin validates forecast model accuracy for time series data in InfluxDB 3 by comparing predicted values with actual observations. The plugin periodically computes error metrics (MSE, MAE, or RMSE), detects anomalies based on error thresholds, and sends notifications when forecast accuracy degrades. It includes debounce logic to suppress transient anomalies and supports multi-channel notifications via the Notification Sender Plugin.

Configuration

Required parameters

Notification parameters

Timing parameters

Authentication parameters

Sender-specific parameters

Slack notifications

Discord notifications

HTTP notifications

SMS notifications (via Twilio)

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

forecast_error_config_scheduler.toml

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

Installation steps

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

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

2. Install required Python packages:

• pandas (for data processing)
• requests (for HTTP notifications)

influxdb3 install package pandas
influxdb3 install package requests

3. Install the Notification Sender Plugin (required):

   # Ensure notifier plugin is available in ~/.plugins/

Trigger setup

Scheduled forecast validation

Run forecast error evaluation periodically:

influxdb3 create trigger \
  --database weather_forecasts \
  --plugin-filename gh:influxdata/forecast_error_evaluator/forecast_error_evaluator.py \
  --trigger-spec "every:30m" \
  --trigger-arguments 'forecast_measurement=temperature_forecast,actual_measurement=temperature_actual,forecast_field=predicted_temp,actual_field=temp,error_metric=rmse,error_thresholds=INFO-"0.5":WARN-"1.0":ERROR-"2.0",window=1h,senders=slack,slack_webhook_url="https://hooks.slack.com/services/YOUR/WEBHOOK/URL"' \
  forecast_validation

Example usage

Example 1: Temperature forecast validation with Slack alerts

Validate temperature forecast accuracy and send Slack notifications:

# Create the trigger
influxdb3 create trigger \
  --database weather_db \
  --plugin-filename gh:influxdata/forecast_error_evaluator/forecast_error_evaluator.py \
  --trigger-spec "every:15m" \
  --trigger-arguments 'forecast_measurement=temp_forecast,actual_measurement=temp_actual,forecast_field=predicted,actual_field=temperature,error_metric=rmse,error_thresholds=INFO-"0.5":WARN-"1.0":ERROR-"2.0":CRITICAL-"3.0",window=30m,senders=slack,slack_webhook_url="https://hooks.slack.com/services/YOUR/WEBHOOK/URL",min_condition_duration=10m' \
  temp_forecast_check

# Write forecast data
influxdb3 write \
  --database weather_db \
  "temp_forecast,location=station1 predicted=22.5"

# Write actual data  
influxdb3 write \
  --database weather_db \
  "temp_actual,location=station1 temperature=21.8"

# Check logs after trigger runs
influxdb3 query \
  --database _internal \
  "SELECT * FROM system.processing_engine_logs WHERE trigger_name = 'temp_forecast_check'"

Expected behavior

• Plugin computes RMSE between forecast and actual values
• If RMSE > 0.5, sends INFO-level notification
• If RMSE > 1.0, sends WARN-level notification
• Only triggers if condition persists for 10+ minutes (debounce)

Notification example:

[WARN] Forecast error alert in temp_forecast.predicted: rmse=1.2. Tags: location=station1

Example 2: Multi-metric validation with multiple channels

Monitor multiple forecast metrics with different notification channels:

# Create trigger with Discord and HTTP notifications
influxdb3 create trigger \
  --database analytics \
  --plugin-filename gh:influxdata/forecast_error_evaluator/forecast_error_evaluator.py \
  --trigger-spec "every:1h" \
  --trigger-arguments 'forecast_measurement=sales_forecast,actual_measurement=sales_actual,forecast_field=predicted_sales,actual_field=sales_amount,error_metric=mae,error_thresholds=WARN-"1000":ERROR-"5000":CRITICAL-"10000",window=6h,senders=discord.http,discord_webhook_url="https://discord.com/api/webhooks/YOUR/WEBHOOK",http_webhook_url="https://your-api.com/alerts",notification_text="[$$level] Sales forecast error: $$metric=$$error (threshold exceeded)",rounding_freq=5min' \
  sales_forecast_monitor

Example 3: SMS alerts for critical forecast failures

Set up SMS notifications for critical forecast accuracy issues:

# Set environment variables (recommended for sensitive data)
export TWILIO_SID="your_twilio_sid"
export TWILIO_TOKEN="your_twilio_token"

# Create trigger with SMS notifications
influxdb3 create trigger \
  --database production_forecasts \
  --plugin-filename gh:influxdata/forecast_error_evaluator/forecast_error_evaluator.py \
  --trigger-spec "every:5m" \
  --trigger-arguments 'forecast_measurement=demand_forecast,actual_measurement=demand_actual,forecast_field=predicted_demand,actual_field=actual_demand,error_metric=mse,error_thresholds=CRITICAL-"100000",window=15m,senders=sms,twilio_from_number="+1234567890",twilio_to_number="+0987654321",notification_text="CRITICAL: Production demand forecast error exceeded threshold. MSE: $$error",min_condition_duration=2m' \
  critical_forecast_alert

Using TOML Configuration Files

This plugin supports using TOML configuration files for complex configurations.

Important Requirements

To use TOML configuration files, you must set the PLUGIN_DIR environment variable in the InfluxDB 3 host environment:

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

Example TOML Configuration

# forecast_error_config_scheduler.toml
forecast_measurement = "temperature_forecast"
actual_measurement = "temperature_actual"
forecast_field = "predicted_temp"
actual_field = "temperature"
error_metric = "rmse"
error_thresholds = 'INFO-"0.5":WARN-"1.0":ERROR-"2.0":CRITICAL-"3.0"'
window = "1h"
senders = "slack"
slack_webhook_url = "https://hooks.slack.com/services/YOUR/WEBHOOK/URL"
min_condition_duration = "10m"
rounding_freq = "1min"
notification_text = "[$$level] Forecast validation alert: $$metric=$$error in $$measurement.$$field"

# Authentication (use environment variables instead when possible)
influxdb3_auth_token = "your_token_here"

Create trigger using TOML config

influxdb3 create trigger \
  --database weather_db \
  --plugin-filename forecast_error_evaluator.py \
  --trigger-spec "every:30m" \
  --trigger-arguments config_file_path=forecast_error_config_scheduler.toml \
  forecast_validation_trigger

Code overview

Files

• forecast_error_evaluator.py: The main plugin code containing scheduler handler for forecast validation
• forecast_error_config_scheduler.toml: Example TOML configuration file

Logging

Logs are stored in the _internal database (or the database where the trigger is created) in the system.processing_engine_logs table. To view logs:

influxdb3 query --database _internal "SELECT * FROM system.processing_engine_logs WHERE trigger_name = 'your_trigger_name'"

Log columns:

• event_time: Timestamp of the log event
• trigger_name: Name of the trigger that generated the log
• log_level: Severity level (INFO, WARN, ERROR)
• log_text: Message describing validation results or errors

Main functions

process_scheduled_call(influxdb3_local, call_time, args)

Handles scheduled forecast validation tasks. Queries forecast and actual measurements, computes error metrics, and triggers notifications.

Key operations:

1. Parses configuration from arguments or TOML file
2. Queries forecast and actual measurements within time window
3. Aligns timestamps using rounding frequency
4. Computes specified error metric (MSE, MAE, or RMSE)
5. Evaluates thresholds and applies debounce logic
6. Sends notifications via configured channels

compute_error_metric(forecast_values, actual_values, metric_type)

Core error computation engine that calculates forecast accuracy metrics.

Supported error metrics:

• mse: Mean Squared Error
• mae: Mean Absolute Error
• rmse: Root Mean Squared Error (square root of MSE)

evaluate_thresholds(error_value, threshold_config)

Evaluates computed error against configured thresholds to determine alert level.

Returns alert level based on threshold ranges:

• INFO: Informational threshold exceeded
• WARN: Warning threshold exceeded
• ERROR: Error threshold exceeded
• CRITICAL: Critical threshold exceeded

Troubleshooting

Common issues

Issue: No overlapping timestamps between forecast and actual data

Solution: Check that both measurements have data in the specified time window and use rounding_freq for alignment:

influxdb3 query --database mydb "SELECT time, field_value FROM forecast_measurement WHERE time >= now() - 1h"
influxdb3 query --database mydb "SELECT time, field_value FROM actual_measurement WHERE time >= now() - 1h"

Issue: Notifications not being sent

Solution: Verify the Notification Sender Plugin is installed and webhook URLs are correct:

# Check if notifier plugin exists
ls ~/.plugins/notifier_plugin.py

# Test webhook URL manually
curl -X POST "your_webhook_url" -d '{"text": "test message"}'

Issue: Error threshold format not recognized

Solution: Use proper threshold format with level prefixes:

--trigger-arguments 'error_thresholds=INFO-"0.5":WARN-"1.0":ERROR-"2.0":CRITICAL-"3.0"'

Issue: Environment variables not loaded

Solution: Set environment variables before starting InfluxDB:

export INFLUXDB3_AUTH_TOKEN="your_token"
export TWILIO_SID="your_sid"
influxdb3 serve --plugin-dir ~/.plugins

Debugging tips

1. Check data availability in both measurements:

   influxdb3 query --database mydb \
     "SELECT COUNT(*) FROM forecast_measurement WHERE time >= now() - window"

2. Verify timestamp alignment with rounding frequency:

   --trigger-arguments 'rounding_freq=5min'

3. Test with shorter windows for faster debugging:

   --trigger-arguments 'window=10m,min_condition_duration=1m'

4. Monitor notification delivery in logs:

   influxdb3 query --database _internal \
     "SELECT * FROM system.processing_engine_logs WHERE log_text LIKE '%notification%'"

Performance considerations

• Data alignment: Use appropriate rounding_freq to balance accuracy and performance
• Window size: Larger windows increase computation time but provide more robust error estimates
• Debounce duration: Balance between noise suppression and alert responsiveness
• Notification throttling: Built-in retry logic prevents notification spam
• Memory usage: Plugin processes data in pandas DataFrames - consider memory for large datasets

Report an issue

For plugin issues, see the Plugins repository issues page.

Find support for InfluxDB 3 Enterprise

The InfluxDB Discord server is the best place to find support for InfluxDB 3 Core and InfluxDB 3 Enterprise. For other InfluxDB versions, see the Support and feedback options.