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The study aims to enhance ARIMA forecasting by employing parallelized grid search for efficient hyperparameter selection.

April 17, 2026Open Access

Evaluating Parallel Grid Search for Arima Hyperparameter Selection in Financial Time Series

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This work demonstrates improved forecasting accuracy and speed in financial time series using parallelized ARIMA parameter selection.

The study aims to enhance ARIMA forecasting by employing parallelized grid search for efficient hyperparameter selection.
Developed an automated ARIMA pipeline integrating stationarity testing.
Implemented parallelized grid search for hyperparameter optimization.
Evaluated the approach on three Brazilian stocks over various time horizons.
Achieved a geometric mean speedup of 1.74×, up to 6.58× for medium-length series.
Maintained forecasting accuracy below 3% MAPE for medium-term forecasts.
Showed that parallel efficiency varies significantly with problem scale, especially in medium-length series.

Lima et al. (2026) studied this question.

View Full Paper

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April 17, 2026Open Access

View Full Paper

Ask AI

Bookmark

Authors

This work demonstrates improved forecasting accuracy and speed in financial time series using parallelized ARIMA parameter selection.

The study aims to enhance ARIMA forecasting by employing parallelized grid search for efficient hyperparameter selection.
Developed an automated ARIMA pipeline integrating stationarity testing.
Implemented parallelized grid search for hyperparameter optimization.
Evaluated the approach on three Brazilian stocks over various time horizons.
Achieved a geometric mean speedup of 1.74×, up to 6.58× for medium-length series.
Maintained forecasting accuracy below 3% MAPE for medium-term forecasts.
Showed that parallel efficiency varies significantly with problem scale, especially in medium-length series.

Lima et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark