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on Forecasting |
| By: | Tony Chernis; Niko Hauzenberger; Florian Huber; Gary Koop; James Mitchell |
| Abstract: | Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. The key ingredient in BPS is a ``synthesis'' function. This is typically specified parametrically as a dynamic linear regression. In this paper, we develop a nonparametric treatment of the synthesis function using regression trees. We show the advantages of our tree-based approach in two macroeconomic forecasting applications. The first uses density forecasts for GDP growth from the euro area's Survey of Professional Forecasters. The second combines density forecasts of US inflation produced by many regression models involving different predictors. Both applications demonstrate the benefits -- in terms of improved forecast accuracy and interpretability -- of modeling the synthesis function nonparametrically. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.12671&r=for |
| By: | Xitai Yu |
| Abstract: | The ARIMA (Autoregressive Integrated Moving Average model) has extensive applications in the field of time series forecasting. However, the predictive performance of the ARIMA model is limited when dealing with data gaps or significant noise. Based on previous research, we have found that cubic spline interpolation performs well in capturing the smooth changes of stock price curves, especially when the market trends are relatively stable. Therefore, this paper integrates the two approaches by taking the time series data in stock trading as an example, establishes a time series forecasting model based on cubic spline interpolation and ARIMA. Through validation, the model has demonstrated certain guidance and reference value for short-term time series forecasting. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.10759&r=for |