Calculating a Running Performance Score for a Time-Series Setpoint Following

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What You Will Learn

In this tutorial, you will master the art of calculating a running performance score for a time-series setpoint following in Python. By understanding this concept, you will be able to monitor system performance effectively and make informed decisions based on the calculated scores.

Introduction to the Problem and Solution

When dealing with time-series data, it is essential to evaluate performance based on setpoints. By continuously assessing the deviation of actual values from desired setpoints over time, we can gauge system efficiency and make informed decisions. To tackle this challenge, we will develop a function that takes actual values and setpoints as inputs, computes deviations at each data point, and aggregates these discrepancies into an overall performance score.

Code

# Import necessary libraries
import numpy as np  # For numerical operations

# Function to calculate running performance score
def calculate_performance_score(actual_values, setpoints):
    scores = [abs(actual_val - setpoint) for actual_val, setpoint in zip(actual_values, setpoints)]

    # Calculate average score over all data points
    avg_score = np.mean(scores)

    return avg_score

# Example usage
actual_data = [10.2, 11.5, 9.8]
set_points = [10.0, 11.0 ,10.5]

performance_score = calculate_performance_score(actual_data ,set_points)

print(performance_score)  # Output: The calculated running performance score

# Copyright PHD

Note: You can customize or enhance the code as per your specific requirements.

Explanation

In this solution: – We import NumPy for numerical operations. – The calculate_performance_score function iterates through each pair of actual values and corresponding setpoints. – It computes the absolute deviation between them and stores them in scores. – Finally, it calculates the average of all deviations as the running performance score.

  1. How do I interpret the calculated performance score?

  2. The calculated performance score provides an average measure of deviation between actual values and desired setpoints across all data points.

  3. Can I use other metrics instead of mean deviation?

  4. Absolutely! Depending on your needs or domain requirements, you can adjust the calculation method within the function accordingly.

  5. Is NumPy necessary for this implementation?

  6. While not mandatory for basic calculations like mean computation here; however using NumPy enhances efficiency especially with larger datasets.

  7. Can I apply this concept to real-time streaming data?

  8. Yes! Continuously updating new data points into existing lists allows seamless integration with real-time applications.

  9. How does altering weighting affect results?

  10. Introducing weights during deviation calculation (e.g., squared errors) enables emphasizing specific deviations more than others which can be beneficial based on application context.

Conclusion

Mastering how to calculate running performance scores is pivotal when working with time-series datasets in Python. By implementing such algorithms, you gain valuable insights into system behavior over time which significantly aids decision-making processes.

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