v0.0.11

[AzulGarza]

Features

• TiRex Foundation Model: Added the TiRex time series foundation model. See #77. Example:

import pandas as pd
from timecopilot.models.foundational.tirex import TiRex

df = pd.read_csv(
    "https://timecopilot.s3.amazonaws.com/public/data/algeria_exports.csv", 
    parse_dates=["ds"],
)
model = TiRex()
fcst = model.forecast(df, h=12)

• Toto Model: Added the Toto time series model. See #78. Example:

import pandas as pd
from timecopilot.models.foundational.toto import Toto

df = pd.read_csv(
    "https://timecopilot.s3.amazonaws.com/public/data/algeria_exports.csv", 
    parse_dates=["ds"],
)
model = Toto()
fcst = model.forecast(df, h=12)

• Optional freq Parameter: The freq parameter is now optional in all forecast and cross-validation methods. If not provided, frequency is inferred automatically from the data. See #96. Example:

import pandas as pd
from timecopilot.models.benchmarks import SeasonalNaive

df = pd.read_csv(
    "https://timecopilot.s3.amazonaws.com/public/data/algeria_exports.csv", 
    parse_dates=["ds"],
)
model = SeasonalNaive()
# freq is now optional
fcst = model.forecast(df, h=12)

• Improved Model Docstrings: All foundational and statistical model constructors are now fully documented, with clear parameter explanations and references to official sources. See #93 and #94.

• Comprehensive Module Docstrings: Added module-level docstrings to improve API documentation and usability. See #82.

• TimeCopilotForecaster Documentation: Documented the TimeCopilotForecaster class, including its constructor and methods, to clarify its unified, multi-model forecasting and cross-validation interface. See #97. Example:

import pandas as pd
from timecopilot.forecaster import TimeCopilotForecaster
from timecopilot.models.benchmarks.prophet import Prophet
from timecopilot.models.benchmarks.stats import AutoARIMA, SeasonalNaive
from timecopilot.models.foundational.toto import Toto

df = pd.read_csv(
    "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv",
    parse_dates=["ds"],
)
tcf = TimeCopilotForecaster(
    models=[
        AutoARIMA(),
        SeasonalNaive(),
        Prophet(),
        Toto(context_length=256),
    ]
)

fcst_df = tcf.forecast(df=df, h=12)
cv_df = tcf.cross_validation(df=df, h=12)

Tests

• Parallel Test Execution: Added pytest-xdist to enable running tests in parallel, speeding up CI and local test runs. See #75.

Fixes

• Documentation Improvements: Enhanced documentation for all models and constructors, ensuring clarity and consistency across the codebase. See #93, #94, and #82.

• S3 Data Source: All example and test data now use S3 URLs for consistency and reproducibility. See #73.

Full Changelog: v0.0.10...v0.0.11

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This discussion was created from the release v0.0.11.