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on Forecasting |
| By: | Jonathan Berrisch; Florian Ziel |
| Abstract: | This paper presents a new method for combining (or aggregating or ensembling) multivariate probabilistic forecasts, taking into account dependencies between quantiles and covariates through a smoothing procedure that allows for online learning. Two smoothing methods are discussed: dimensionality reduction using Basis matrices and penalized smoothing. The new online learning algorithm generalizes the standard CRPS learning framework into multivariate dimensions. It is based on Bernstein Online Aggregation (BOA) and yields optimal asymptotic learning properties. We provide an in-depth discussion on possible extensions of the algorithm and several nested cases related to the existing literature on online forecast combination. The methodology is applied to forecasting day-ahead electricity prices, which are 24-dimensional distributional forecasts. The proposed method yields significant improvements over uniform combination in terms of continuous ranked probability score (CRPS). We discuss the temporal evolution of the weights and hyperparameters and present the results of reduced versions of the preferred model. A fast C++ implementation of all discussed methods is provided in the R-Package profoc. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.10019&r=for |
| By: | Thomas Wong; Prof. Mauricio Barahona |
| Abstract: | In this paper, we explore the use of different feature engineering and dimensionality reduction methods in multi-variate time-series modelling. Using a feature-target cross correlation time series dataset created from Numerai tournament, we demonstrate under over-parameterised regime, both the performance and predictions from different feature engineering methods converge to the same equilibrium, which can be characterised by the reproducing kernel Hilbert space. We suggest a new Ensemble method, which combines different random non-linear transforms followed by ridge regression for modelling high dimensional time-series. Compared to some commonly used deep learning models for sequence modelling, such as LSTM and transformers, our method is more robust (lower model variance over different random seeds and less sensitive to the choice of architecture) and more efficient. An additional advantage of our method is model simplicity as there is no need to use sophisticated deep learning frameworks such as PyTorch. The learned feature rankings are then applied to the temporal tabular prediction problem in the Numerai tournament, and the predictive power of feature rankings obtained from our method is better than the baseline prediction model based on moving averages |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.07925&r=for |
| By: | Jing Xie |
| Abstract: | Many central banks and government agencies use nowcasting techniques to obtain policy relevant information about the business cycle. Existing nowcasting methods, however, have two critical shortcomings for this purpose. First, in contrast to machine-learning models, they do not provide much if any guidance on selecting the best explantory variables (both high- and low-frequency indicators) from the (typically) larger set of variables available to the nowcaster. Second, in addition to the selection of explanatory variables, the order of the autoregression and moving average terms to use in the baseline nowcasting regression is often set arbitrarily. This paper proposes a simple procedure that simultaneously selects the optimal indicators and ARIMA(p, q) terms for the baseline nowcasting regression. The proposed AS-ARIMAX (Adjusted Stepwise Autoregressive Moving Average methods with exogenous variables) approach significantly reduces out-of-sample root mean square error for nowcasts of real GDP of six countries, including India, Argentina, Australia, South Africa, the United Kingdom, and the United States. |
| Keywords: | Nowcasting; Mixed Frequency; Forecasting; Business Cycles; selection procedure; Annex I. AS-ARIMAX procedure; nowcasting method; evaluation comparison; baseline model; Global |
| Date: | 2023–03–03 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2023/045&r=for |