# A Non Parametric Model for the Forecasting of the Venezuelan Oil Prices

**Authors:** Sabatino Costanzo, Loren Trigo, Wafaa Dehne, Hender Prato  
**arXiv:** [0708.3829](https://arxiv.org/abs/0708.3829)  
**Date:** 2007-08-28  
**Summary page:** https://www.summarizepaper.com/en/paper/0708.3829v1/

## Key points

- The paper proposes a non-parametric neural net model for forecasting Venezuelan crude oil prices.
- Inputs of the neural net are selected based on a dynamic system model of oil prices by Mashayekhi (1995, 2001).
- The authors describe in detail the process of data collection and pre-processing before running the neural network.
- The performance of the model is evaluated using two criteria: Excess Profitability test by Anatoliev and Gerko (2005) and characteristics of equity curve generated by a trading strategy based on the neural net predictions.
- The non-parametric approach used in this model allows for more flexibility in capturing complex patterns in data.
- Selection of inputs is based on a well-established dynamic system model, which adds credibility to their approach.
- Statistical tests are used to measure predictability and examine characteristics of equity curves generated by trading strategies based on predictions to evaluate model performance.
- Results show that this model outperforms other benchmark models in terms of both predictability and profitability.

## Summary

This paper proposes a non-parametric neural net model for forecasting the prices of Venezuelan crude oil. The inputs of the neural net are selected based on a dynamic system model of oil prices by Mashayekhi (1995, 2001). The authors describe in detail the process of data collection and pre-processing before running the neural network. The performance of the model is evaluated using two criteria: the Excess Profitability test by Anatoliev and Gerko (2005) and the characteristics of the equity curve generated by a trading strategy based on the neural net predictions. The authors explain that their model is unique because it uses a non-parametric approach to forecast oil prices, which allows for more flexibility in capturing complex patterns in data. They also note that their selection of inputs is based on a well-established dynamic system model, which adds credibility to their approach. To evaluate their model's performance, they use statistical tests to measure predictability and examine the characteristics of equity curves generated by trading strategies based on their predictions. Their results show that their model outperforms other benchmark models in terms of both predictability and profitability. Overall, this paper provides valuable insights into how non-parametric neural net models can be used to forecast oil prices. It also highlights the importance of selecting appropriate inputs and evaluating performance using multiple criteria to ensure accuracy and reliability when predicting future trends in oil prices.