Oil Price Forecasting Using a Time-Varying Approach

Abstract

The international crude oil market plays an important role in the global economy. This paper uses a variable time window and the polynomial decomposition method to define the trend term of time series and proposes a crude oil price forecasting method based on time-varying trend decomposition to describe the changes in trends over time and forecast crude oil prices. First, to characterize the time-varying characteristics of crude oil price trends, the basic concepts of post-position intervals, pre-position intervals and time-varying windows are defined. Second, a crude oil price series is decomposed with a time-varying window to determine the best fitting results. The parameter vector is used as a time-varying trend. Then, to quantitatively describe the continuation of the time-varying trend, the concept of the trend threshold is defined, and a corresponding algorithm for selecting the trend threshold is given. Finally, through the predicted trend thresholds, the historical reference data are selected, and the time-varying trend is combined to complete the crude oil price forecast. Through empirical research, it is found that the time-varying trend prediction model proposed in this paper achieves a better prediction than several common models. These results can provide suggestions and references for investors in the international crude oil market to understand the trends of oil prices and improve their investment decisions.

An Auxiliary Index for Reducing Brent Crude Investment Risk—Evaluating the Price Relationships between Brent Crude and Commodities

“…Good prediction results can be provided by using the above model based on the near linear relationships [4]. Zhao et al (2020) proposed a crude oil price forecasting method based on time-varying trend decomposition to describe the changes in trends over time and forecast crude oil prices [16].…”

“…Mean squared prediction error (MSPE) and directional accuracy ratio (DAR) are popular metrics for forecasting in economics and finance. Zhao et al (2020), Gao and Lei (2017) also applied both metrics to evaluate the performance of crude oil price forecasting [16,38]. They are also useful metrics for picking out a commodity as the best auxiliary investing tool for predicting Brent Crude oil price.…”

A Dynamic Level Technical Indicator Model for Oil Price Forecasting

“…Chi and Peng [20] studied the relationship among various technical indicators and using self-organizing map and fuzzy neural network. On the prediction of oil prices, the diverse approaches proposed by other members of the research community include: the use of sentiment on news article [21], autoregressive integrated moving average (ARIMA) model [22], a hybrid of wavelet or Commodity Futures Prices and artificial neural networks [23] [25], deep learning based models [24], statistical learning method [26], time-varying approach [27], gray wave forecasting method and optimization via bagging ensemble models [29].…”

Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression

“…[5][6][7]. As a result, forecasting methods allowing for the state-space of the model to vary in time have gained much attention [2,3,[8][9][10]. Detailed analyses and reviews of how these variables can play an important role as crude oil spot price predictors have been presented in numerous other papers or in review articles solely devoted to this problem [2,[11][12][13][14].…”