Optimizing ARIMA for Enhanced Time Series Predictions: Strategies and Techniques

Enhancing the Precision of Time Series Prediction Using ARIMA

Abstract:

This paper explore and enhance the accuracy of time series predictions through an in-depth study on Autoregressive Integrated Moving Average ARIMA. Time series prediction plays a vital role across various fields, including finance, economics, healthcare, and environmental studies, where accurate forecasting can facilitate decision-making processes.

To improve ARIMA model's efficiency for predicting future values, several strategies are employed:

1. Parameter Tuning: The key to successful ARIMA modeling lies in selecting the appropriate parameters p,d,q. Through rigorous experimentation on datasets like the rPassengers dataset and the IBM stock dataset, optimal parameter settings were identified that minimized prediction errors.

2. Feature Engineering: Incorporating domn-specific knowledge into feature selection enhances the model's interpretability and predictive power. For instance, adding lag features or seasonal indexes could provide insights into temporal depencies unique to specific industries.

3. Model Validation: Utilizing robust validation techniques like time series cross-validation ensures that predictions are reliable across different segments of the dataset. This method prevents overfitting and underfitting, crucial for forecasting accuracy.

4. Error Analysis: Post-prediction error analysis helps in understanding where predictions deviate from actual values. Techniques such as residual plots can identify patterns or anomalies requiring further model adjustments.

5. Advanced Techniques Integration: Combining ARIMA with otherlike Random Forests or Neural Networks improves prediction accuracy, leveraging the strengths of multiple methods to overcome limitations of individual approaches.

6. Handling Missing Data: Implementing strategies for dealing with missing data points ensures that the time series does not lose valuable information necessary for accurate predictions. Techniques such as interpolation or imputation provide alternative values without compromising forecast quality.

7. Seasonality Adjustment: Incorporating seasonal adjustments is vital in datasets exhibiting seasonal trs. The ARIMA model's capability to handle seasonality enhances its predictive accuracy, making it more applicable across diverse time series problems.

In , this paper emphasizes the importance of selecting appropriate parameters and employing advanced techniques for enhancing the precision of ARIMn time series prediction. The strategies discussed here can significantly improve forecasting accuracy, thereby ding decision-makers in various fields with reliable predictions based on historical data trs.

Keywords: Time Series Prediction; ARIMA; Forecasting Techniques; Data Mining; Feature Engineering
This article is reproduced from: https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2024.1421707/full

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