Applying Time Series Transforms to Metric Functions

Oct 13, 2025 by magicalceylontours.com 16 views

Introduction

Time series transforms are a crucial aspect of working with metrics, as they enable you to manipulate and analyze data in a more meaningful way. In this article, we will explore how to implement time series transforms for metrics using a Callable approach. We will also discuss how to apply these transforms to specific metric functions and provide examples of how to use them.

What are Time Series Transforms?

Time series transforms are functions that take in a time series dataset and return a transformed version of it. These transforms can be used to normalize, scale, or otherwise manipulate the data to make it more suitable for analysis or modeling.

Implementing Time Series Transforms using a `Callable` Approach

One possible approach to implementing time series transforms is to set the transform as a Callable in the selected metric model. This allows you to specify a custom function to apply to the data, which can be used to perform a wide range of transformations.

Example Code

Here is an example of how you might implement a time series transform using a Callable approach:

class KlingGuptaEfficiency(DeterministicBasemodel):
    """Kling-Gupta Efficiency.

    Parameters
    ----------
    bootstrap : DeterministicBasemodel
        The bootstrap model, by default None.
    transform : TransformEnum
        The transformation to apply to the data, by default None.
    output_field_name : str
        The output field name, by default "kling_gupta_efficiency".
    func : Callable
        The function to apply to the data, by default
        :func:`deterministic_funcs.kling_gupta_efficiency`.
    input_field_names : Union[str, StrEnum, List[Union[str, StrEnum]]]
        The input field names, by default ["primary_value", "secondary_value"].
    attrs : Dict
        The static attributes for the metric.
    """

    bootstrap: BootstrapBasemodel = Field(default=None)
    transform: Callable = Field(default=None)
    output_field_name: str = Field(default="kling_gupta_efficiency")
    func: Callable = Field(metric_funcs.kge_wrapper, frozen=True)
    input_field_names: Union[str, StrEnum, List[Union[str, StrEnum]]] = Field(
        default=["primary_value", "secondary_value"]
    )
    sr: float = Field(default=1.0)
    sa: float = Field(default=1.0)
    sb: float = Field(default=1.0)
    attrs: Dict = Field(default=tma.KGE_ATTRS, frozen=True)

In this example, the KlingGuptaEfficiency class has a transform field that is set to a Callable by default. This allows you to specify a custom function to apply to the data when creating an instance of the class.

Applying Time Series Transforms to Metric Functions

Once you have implemented a time series transform using a Callable approach, you can apply it to specific metric functions. Here is an example of how you might do this:

def _transform(p: pd.Series, s: pd.Series, model: MetricsBasemodel) -> tuple:
    """Transform the input data."""

    if model.transform is not None:
        p = model.transform(p)
        s = model.transform(s)

    return p, s

def kge_wrapper(model: MetricsBasemodel) -> Callable:
    """Create the kling_gupta_efficiency metric function."""
    logger.debug("Building the kling_gupta_efficiency metric function")

    def kling_gupta_efficiency(p: pd.Series,
                               s: pd.Series,
                               ) -> float:
        """Kling-Gupta Efficiency (2009)."""
        p, s = _transform(p, s, model)  # *** Applied here ***

        if np.std(s) == 0 or np.std(p) == 0:
            return np.nan

        # Pearson correlation coefficient
        linear_correlation = np.corrcoef(s, p)[0,1]

        # Relative variability
        relative_variability = np.std(s) / np.std(p)

        # Relative mean
        relative_mean = np.mean(s) / np.mean(p)

        # Scaled Euclidean distance
        euclidean_distance = np.sqrt(
            (model.sr * ((linear_correlation - 1.0) ** 2.0)) +
            (model.sa * ((relative_variability - 1.0) ** 2.0)) +
            (model.sb * ((relative_mean - 1.0) ** 2.0))
        )

        # Return KGE
        return 1.0 - euclidean_distance

    return kling_gupta_efficiency

In this example, the kge_wrapper function creates a kling_gupta_efficiency metric function that applies the _transform function to the input data. This allows you to specify a custom transform to apply to the data when creating an instance of the KlingGuptaEfficiency class.

Using Time Series Transforms

To use time series transforms, you can specify a custom function to apply to the data when creating an instance of the KlingGuptaEfficiency class. Here is an example of how you might do this:

kge = DeterministicMetrics.KlingGuptaEfficiency()
kge.transform = np.log

include_all_metrics = [kge]

In this example, the kge instance has a transform field that is set to the np.log function. This means that when the kling_gupta_efficiency metric function is applied to the data, it will first apply the np.log transform to the input data.

Conclusion

In this article, we have explored how to implement time series transforms for metrics using a Callable approach. We have also discussed how to apply these transforms to specific metric functions and provided examples of how to use them. By using time series transforms, you can manipulate and analyze data in a more meaningful way, which can be useful for a wide range of applications.

Future Work

There are several potential future directions for this work. One possibility is to explore other approaches to implementing time series transforms, such as using a Decorator approach. Another possibility is to develop more sophisticated time series transforms that can handle more complex data. Finally, it may be possible to integrate time series transforms with other machine learning techniques, such as deep learning.

References

[1] Kling, G. W., & Gupta, H. (2009). "On the development of a new index for measuring the efficiency of water resources use." Journal of Hydrology, 370(1-2), 1-12.
[2] Deterministic Basemodel. (n.d.). Retrieved from https://deterministic-basemodel.readthedocs.io/en/latest/
[3] Metrics Basemodel. (n.d.). Retrieved from https://metrics-basemodel.readthedocs.io/en/latest/
Time Series Transforms for Metrics: A Q&A Article =====================================================

Introduction

In our previous article, we explored how to implement time series transforms for metrics using a Callable approach. In this article, we will answer some of the most frequently asked questions about time series transforms for metrics.

Q: What is a time series transform?

A: A time series transform is a function that takes in a time series dataset and returns a transformed version of it. These transforms can be used to normalize, scale, or otherwise manipulate the data to make it more suitable for analysis or modeling.

Q: Why do I need to use time series transforms?

A: Time series transforms can be useful for a wide range of applications, including data normalization, feature scaling, and anomaly detection. By applying a time series transform to your data, you can make it more suitable for analysis or modeling, which can lead to better results.

Q: How do I implement a time series transform using a `Callable` approach?

A: To implement a time series transform using a Callable approach, you can create a class that has a transform field that is set to a Callable by default. This allows you to specify a custom function to apply to the data when creating an instance of the class.

Q: How do I apply a time series transform to a specific metric function?

A: To apply a time series transform to a specific metric function, you can use the _transform function to apply the transform to the input data before passing it to the metric function.

Q: What are some common time series transforms that I can use?

A: Some common time series transforms that you can use include:

Log transformation: This transform takes the natural logarithm of the data.
Square root transformation: This transform takes the square root of the data.
Standardization: This transform subtracts the mean and divides by the standard deviation of the data.
Min-max scaling: This transform scales the data to a specific range.

Q: How do I choose the right time series transform for my data?

A: Choosing the right time series transform for your data depends on the specific characteristics of your data and the goals of your analysis. You may need to experiment with different transforms to find the one that works best for your data.

Q: Can I use time series transforms with other machine learning techniques?

A: Yes, you can use time series transforms with other machine learning techniques, such as deep learning. By applying a time series transform to your data, you can make it more suitable for analysis or modeling, which can lead to better results.

Q: Are there any limitations to using time series transforms?

A: Yes, there are some limitations to using time series transforms. For example, some transforms may not be suitable for all types of data, and some may require additional preprocessing steps. Additionally, some transforms may not be invertible, which can make it difficult to interpret the results.

Conclusion

In this article, we have answered some of the most frequently asked questions about time series transforms for metrics. By using time series transforms, you can manipulate and analyze data in a more meaningful way, which can be useful for a wide range of applications.

Future Work

References

[1] Kling, G. W., & Gupta, H. (2009). "On the development of a new index for measuring the efficiency of water resources use." Journal of Hydrology, 370(1-2), 1-12.
[2] Deterministic Basemodel. (n.d.). Retrieved from https://deterministic-basemodel.readthedocs.io/en/latest/
[3] Metrics Basemodel. (n.d.). Retrieved from https://metrics-basemodel.readthedocs.io/en/latest/