Document a time series forecasting model

Use the FRED sample dataset to train a simple time series model and document that model with the ValidMind Library.

As part of the notebook, you will learn how to train a simple model while exploring how the documentation process works:

Initializing the ValidMind Library
Loading a sample dataset provided by the library to train a simple time series model
Running a ValidMind test suite to quickly generate documentation about the data and model

About ValidMind

ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

Before you begin

This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

New to ValidMind?

If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

For access to all features available in this notebook, you'll need access to a ValidMind account.

Register with ValidMind

Key concepts

record: A tool tracked in the ValidMind inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.

model: SR 26-2 (which supersedes SR 11-7) defines a model as a "complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates." Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. Within ValidMind, a model is a type of record tracked in the inventory.

documentation, model documentation: A structured and detailed document pertaining to a record, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. Within the realm of risk management, this documentation serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the record's application.

document template: Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite specifying the tests that should be run, and how the results should be displayed. Document templates help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.

documentation template: A default ValidMind document type that serves as a standardized framework for developing and documenting records, including sections designated for record details, data descriptions, test results, and performance metrics. By outlining required documentation and recommended analyses, document templates ensure consistency and completeness across documentation and help guide developers through a systematic development process while promoting comparability and traceability of development outcomes.

test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
dataset: A single dataset that has been initialized in ValidMind with init_dataset().
models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)

parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

Setting up

Install the ValidMind Library

To install the library:

%pip install -q validmind

Initialize the ValidMind Library

Register sample model

Let's first register a sample record (model) for use with this notebook:

In a browser, log in to ValidMind.
In the left sidebar, select Inventory.
Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)
Enter the model details and click Next > to continue to assignment of inventory record stakeholders.
Select your own name under the RECORD OWNER drop-down.
Click Register Model to add the model to your inventory.

Apply documentation template

Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

In the left sidebar that appears for your model, click Documents and select Development.

If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)
Under TEMPLATE, select Time Series Forecasting with ML.
Click Use Template to apply the template.

Get your code snippet

Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.
Click Copy snippet to clipboard.
Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

# Load your model identifier credentials from an `.env` file

%load_ext dotenv
%dotenv .env

# Or replace with your code snippet

import validmind as vm

vm.init(
    # api_host="...",
    # api_key="...",
    # api_secret="...",
    # model="...",
    document="documentation",
)

Initialize the Python environment

Next, let's import the necessary libraries and set up your Python environment for data analysis:

from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split

%matplotlib inline

Preview the documentation template

Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

You will upload documentation and test results unique to your model based on this template later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

vm.preview_template()

Load the sample dataset

The sample dataset used here is provided by the ValidMind library. To be able to use it, you need to import the dataset and load it into a pandas DataFrame, a two-dimensional tabular data structure that makes use of rows and columns:

from validmind.datasets.regression import fred_timeseries 

target_column = fred_timeseries.target_column

print(
    f"Loaded demo dataset with: \n\n\t• Target column: '{target_column}'"
)

raw_df = fred_timeseries.load_data()
raw_df.head()

Document the model

As part of documenting the model with the ValidMind Library, you need to preprocess the raw dataset, initialize some training and test datasets, initialize a model object you can use for testing, and then run the full suite of tests.

Prepocess the raw dataset

Preprocessing performs a number of operations to get ready for the subsequent steps: - Split the dataset: Divide the original dataset into training and test sets for the primary model with an 80/20 split, without shuffling. - Difference the data: Calculate the first difference of the train and test datasets to remove trends and seasonality, then drop any resulting NaN values. - Extract features and target variables: Separate the feature columns (predictors) and the target variable from the differenced train and test datasets.

# Split the raw dataset into training and test sets 
train_df, test_df = train_test_split(raw_df, test_size=0.2, shuffle=False)

# Take the first difference of the training and test sets
train_diff_df = train_df.diff().dropna()
test_diff_df = test_df.diff().dropna()

# Extract the features and target variable from the training set
X_diff_train = train_diff_df.drop(target_column, axis=1)
y_diff_train = train_diff_df[target_column]

# Extract the features and target variable from the test set
X_diff_test = test_diff_df.drop(target_column, axis=1)
y_diff_test = test_diff_df[target_column]

Train random forests and gradient boosting regressor models

This section trains random forest and gradient boosting models on differenced data, transforms predictions back to the original scale, and evaluates model performance using Mean Squared Error (MSE) and R-squared (R²) scores.

The following helper functions are used to post-process predictions and evaluate model performance:

transform_to_levels: Reconstructs the original values from differenced predictions by cumulatively summing them, starting from a given initial value.
evaluate_model: Calculates the Mean Squared Error (MSE) and R-squared (R²) score to evaluate the accuracy of the predictions against the true values.

def transform_to_levels(y_diff_pred, first_value=0): 
    y_pred = [first_value]
    for pred in y_diff_pred:
        y_pred.append(y_pred[-1] + pred)
    return y_pred

def evaluate_model(y_true, y_pred):
    mse = mean_squared_error(y_true, y_pred)
    r2 = r2_score(y_true, y_pred)
    return mse, r2

# Fit the random forest model
model_rf = RandomForestRegressor(n_estimators=1500, random_state=0)
model_rf.fit(X_diff_train, y_diff_train)

# Make predictions on the training and test sets
y_diff_train_pred = model_rf.predict(X_diff_train)
y_diff_test_pred = model_rf.predict(X_diff_test)

# Transform the predictions back to the original scale
y_train_rf_pred = transform_to_levels(y_diff_train_pred, first_value=train_df[target_column].iloc[0])
y_test_rf_pred = transform_to_levels(y_diff_test_pred, first_value=test_df[target_column].iloc[0])

# Evaluate the model's performance on the training and test sets
mse_train, r2_train = evaluate_model(train_df[target_column], y_train_rf_pred)
mse_test, r2_test = evaluate_model(test_df[target_column], y_test_rf_pred)

print(f"Train Mean Squared Error: {mse_train}")
print(f"Train R-Squared: {r2_train}")
print(f"Test Mean Squared Error: {mse_test}")
print(f"Test R-Squared: {r2_test}")

# Fit the gradient boost model
model_gb = GradientBoostingRegressor(n_estimators=1500, random_state=0)
model_gb.fit(X_diff_train, y_diff_train)

# Make predictions on the training and test sets
y_diff_train_pred = model_gb.predict(X_diff_train)
y_diff_test_pred = model_gb.predict(X_diff_test)

# Transform the predictions back to the original scale
y_train_gb_pred = transform_to_levels(y_diff_train_pred, first_value=train_df[target_column].iloc[0])
y_test_gb_pred = transform_to_levels(y_diff_test_pred, first_value=test_df[target_column].iloc[0])

# Evaluate the model's performance on the training and test sets
mse_train, r2_train = evaluate_model(train_df[target_column], y_train_gb_pred)
mse_test, r2_test = evaluate_model(test_df[target_column], y_test_gb_pred)

print(f"Train Mean Squared Error: {mse_train}")
print(f"Train R-Squared: {r2_train}")
print(f"Test Mean Squared Error: {mse_test}")
print(f"Test R-Squared: {r2_test}")

Initialize the ValidMind datasets

Before you can run tests, you must first initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module.

This function takes a number of arguments:

dataset — the raw dataset that you want to provide as input to tests
input_id - a unique identifier that allows tracking what inputs are used when running each individual test
target_column — a required argument if tests require access to true values. This is the name of the target column in the dataset

With all dataframes ready, you can now initialize the ValidMind datasets objects using vm.init_dataset():

vm_raw_ds: contains the raw, unprocessed data with the specified target column.
vm_train_diff_ds: contains the training data with the differenced target column, excluding the first row to remove NaN values caused by differencing.
vm_test_diff_ds: contains the test data with the differenced target column, excluding the first row to remove NaN values caused by differencing.
vm_train_ds: contains the training data, excluding the first row to align with the differenced data.
vm_test_ds: includes the test data split from the raw dataset.

vm_raw_ds = vm.init_dataset(
    input_id="raw_ds",
    dataset=raw_df,
    target_column=target_column,
)

vm_train_diff_ds = vm.init_dataset(
    input_id="train_diff_ds",
    dataset=train_diff_df,
    target_column=target_column,
)

vm_test_diff_ds = vm.init_dataset(
    input_id="test_diff_ds",
    dataset=test_diff_df,
    target_column=target_column,
)

vm_train_ds = vm.init_dataset(
    input_id="train_ds",
    dataset=train_df,
    target_column=target_column,
)

vm_test_ds = vm.init_dataset(
    input_id="test_ds",
    dataset=test_df,
    target_column=target_column,
)

Initialize the ValidMind models

You'll also need to initialize ValidMind model objects (vm_model) that can be passed to other functions for analysis and tests on the data for our models.

Despite the naming convention, ValidMind model objects can be any type of record you want to test, document, validate, or monitor with the ValidMind Library.
From classical statistical and machine learning models, to generative and agentic AI systems and more, the ValidMind model object provides a consistent wrapper around your record so it can be passed as a unified input to any ValidMind test or test suite, with results sent directly to the ValidMind Platform.

Initialize your model object with vm.init_model():

vm_model_rf = vm.init_model(
    model_rf,
    input_id="random_forests_model",
)

vm_model_gb = vm.init_model(
    model_gb,
    input_id="gradient_boosting_model",
)

Assign predictions to the datasets

We can now use the assign_predictions() method from the Dataset object to link existing predictions to any model. If no prediction values are passed, the method will compute predictions automatically:

vm_train_ds.assign_predictions(
    model=vm_model_rf,
    prediction_values=y_train_rf_pred,
)

vm_test_ds.assign_predictions(
    model=vm_model_rf,
    prediction_values=y_test_rf_pred,
)

vm_train_ds.assign_predictions(
    model=vm_model_gb,
    prediction_values=y_train_gb_pred,
)

vm_test_ds.assign_predictions(
    model=vm_model_gb,
    prediction_values=y_test_gb_pred,
)

from validmind.utils import preview_test_config

test_config = fred_timeseries.get_demo_test_config()
preview_test_config(test_config)

Run data validation tests

test = vm.tests.run_test(
    "validmind.data_validation.TimeSeriesDescription",
    input_grid={
        "dataset": ["raw_ds", "train_diff_ds", "test_diff_ds", "train_ds", "test_ds"],
    },
)
test.log()

test = vm.tests.run_test(
    "validmind.data_validation.TimeSeriesLinePlot",
    input_grid={
        "dataset": ["raw_ds"],
    },
)
test.log()

test = vm.tests.run_test(
    "validmind.data_validation.TimeSeriesMissingValues",
    input_grid={
        "dataset": ["raw_ds", "train_diff_ds", "test_diff_ds", "train_ds", "test_ds"],
    },
)
test.log()

test = vm.tests.run_test(
    "validmind.data_validation.SeasonalDecompose",
    input_grid={
        "dataset": ["raw_ds"],
    },
)
test.log()

test = vm.tests.run_test(
    "validmind.data_validation.TimeSeriesDescriptiveStatistics",
    input_grid={
        "dataset": ["train_diff_ds", "test_diff_ds"],
    },
)
test.log()

test = vm.tests.run_test(
    "validmind.data_validation.TimeSeriesOutliers",
    input_grid={
        "dataset": ["train_diff_ds", "test_diff_ds"],
    },
    params={
        "zscore_threshold": 4
    }
)
test.log()

test = vm.tests.run_test(
    "validmind.data_validation.TimeSeriesHistogram",
    input_grid={
        "dataset": [ "train_diff_ds", "test_diff_ds"],
    },
    params={
        "nbins": 100
    }
)
test.log()

test = vm.tests.run_test(
    "validmind.data_validation.DatasetSplit",
    inputs={
        "datasets": ["train_diff_ds", "test_diff_ds"],
    }
)
test.log()

Run model validation tests

test = vm.tests.run_test(
    "validmind.model_validation.ModelMetadata",
    input_grid={
        "model": ["random_forests_model", "gradient_boosting_model"],
    }
)
test.log()

test = vm.tests.run_test(
    "validmind.model_validation.sklearn.RegressionErrors",
    input_grid={
        "dataset": ["train_ds", "test_ds"],
        "model": ["random_forests_model", "gradient_boosting_model"],
    }
)
test.log()

test = vm.tests.run_test(
    "validmind.model_validation.sklearn.RegressionR2Square",
    input_grid={
        "dataset": ["train_ds", "test_ds"],
        "model": ["random_forests_model", "gradient_boosting_model"],
    }
)
test.log()

test = vm.tests.run_test(
    "validmind.model_validation.TimeSeriesR2SquareBySegments:train_data",
    input_grid={
        "dataset": ["train_ds"],
        "model": ["random_forests_model", "gradient_boosting_model"],
    }
)
test.log()

test = vm.tests.run_test(
    "validmind.model_validation.TimeSeriesR2SquareBySegments:test_data",
    input_grid={
        "dataset": ["test_ds"],
        "model": ["random_forests_model", "gradient_boosting_model"],
    },
    params={
        "segments":{
            "start_date": ["2012-11-01","2018-02-01"],
            "end_date": ["2018-01-01","2023-03-01"]
        }
    }
)
test.log()

test = vm.tests.run_test(
    "validmind.model_validation.TimeSeriesPredictionsPlot",
    input_grid={
        "dataset": ["train_ds", "test_ds"],
        "model": ["random_forests_model", "gradient_boosting_model"],
    }
)
test.log()

test = vm.tests.run_test(
    "validmind.model_validation.TimeSeriesPredictionWithCI",
    input_grid={
        "dataset": ["train_ds", "test_ds"],
        "model": ["random_forests_model", "gradient_boosting_model"],
    }
)
test.log()

test = vm.tests.run_test(
    "validmind.model_validation.ModelPredictionResiduals",
    input_grid={
        "dataset": ["train_ds", "test_ds"],
        "model": ["random_forests_model", "gradient_boosting_model"],
    }
)
test.log()

test = vm.tests.run_test(
    "validmind.model_validation.sklearn.FeatureImportance",
    input_grid={
        "dataset": ["train_ds", "test_ds"],
        "model": ["random_forests_model", "gradient_boosting_model"],
    }
)
test.log()

test = vm.tests.run_test(
    "validmind.model_validation.sklearn.PermutationFeatureImportance",
    input_grid={
        "dataset": ["train_ds", "test_ds"],
        "model": ["random_forests_model", "gradient_boosting_model"],
    }
)
test.log()

Next steps

You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation.

Work with your documentation

From the Inventory in the ValidMind Platform, go to the model you registered earlier. (Learn more: Working with the inventory)
In the left sidebar that appears for your model, click Development under Documents.

What you see is the full draft of your documentation in a more easily consumable version. From here, you can make qualitative edits to documentation, view guidelines, collaborate with validators, and submit your documentation for approval when it's ready. (Learn more: Working with documentation)

Discover more learning resources

We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

Or, visit our documentation to learn more about ValidMind.

Upgrade ValidMind

After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.

Retrieve the information for the currently installed version of ValidMind:

%pip show validmind

If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

%pip install --upgrade validmind

You may need to restart your kernel after running the upgrade package for changes to be applied.