共查询到20条相似文献,搜索用时 15 毫秒
1.
《International Journal of Forecasting》2019,35(4):1424-1431
This paper describes the preprocessing and forecasting methods used by team Orbuculum during the qualifying match of the Global Energy Forecasting Competition 2017 (GEFCom2017). Tree-based algorithms (gradient boosting and quantile random forest) and neural networks made up an ensemble. The ensemble prediction quantiles were obtained by a simple averaging of the ensemble members’ prediction quantiles. The result shows a robust performance according to the pinball loss metric, with the ensemble model achieving third place in the qualifying match of the competition. 相似文献
2.
《International Journal of Forecasting》2022,38(4):1555-1561
Machine learning (ML) methods are gaining popularity in the forecasting field, as they have shown strong empirical performance in the recent M4 and M5 competitions, as well as in several Kaggle competitions. However, understanding why and how these methods work well for forecasting is still at a very early stage, partly due to their complexity. In this paper, I present a framework for regression-based ML that provides researchers with a common language and abstraction to aid in their study. To demonstrate the utility of the framework, I show how it can be used to map and compare ML methods used in the M5 Uncertainty competition. I then describe how the framework can be used together with ablation testing to systematically study their performance. Lastly, I use the framework to provide an overview of the solution space in regression-based ML forecasting, identifying areas for further research. 相似文献
3.
《International Journal of Forecasting》2014,30(2):369-374
This report discusses methods for forecasting hourly loads of a US utility as part of the load forecasting track of the Global Energy Forecasting Competition 2012 hosted on Kaggle. The methods described (gradient boosting machines and Gaussian processes) are generic machine learning/regression algorithms, and few domain-specific adjustments were made. Despite this, the algorithms were able to produce highly competitive predictions, which can hopefully inspire more refined techniques to compete with state-of-the-art load forecasting methodologies. 相似文献
4.
《International Journal of Forecasting》2022,38(4):1400-1404
This work presents key insights on the model development strategies used in our cross-learning-based retail demand forecast framework. The proposed framework outperforms state-of-the-art univariate models in the time series forecasting literature. It has achieved 17th position in the accuracy track of the M5 forecasting competition, which is among the top 1% of solutions. 相似文献
5.
Richard T. Baillie Chaleampong KongcharoenGeorge Kapetanios 《International Journal of Forecasting》2012,28(1):46
This paper considers the effects on multi-step prediction of using semiparametric local Whittle estimators rather than MLE for long memory ARFIMA models. We consider various representations of the minimum MSE predictor with known parameters. We then conduct a detailed simulation study for when the true parameters are replaced with estimates. The predictor based on MLE is found to be superior, in the MSE sense, to the predictor based on the two-step local Whittle estimation. The “optimal” bandwidth local Whittle estimator produces worse predictions than the local Whittle using an agnostic bandwidth of the square root of the sample size. 相似文献
6.
《International Journal of Forecasting》2022,38(4):1473-1481
The prevalence of approaches based on gradient boosted trees among the top contestants in the M5 competition is potentially the most eye-catching result. Tree-based methods out-shone other solutions, in particular deep learning-based solutions. The winners in both tracks of the M5 competition heavily relied on them. This prevalence is even more remarkable given the dominance of other methods in the literature and the M4 competition. This article tries to explain why tree-based methods were so widely used in the M5 competition. We see possibilities for future improvements of tree-based models and then distill some learnings for other approaches, including but not limited to neural networks. 相似文献
7.
In this paper we challenge the traditional design used for forecasting competitions. We implement an online competition with a public leaderboard that provides instant feedback to competitors who are allowed to revise and resubmit forecasts. The results show that feedback significantly improves forecasting accuracy. 相似文献
8.
To forecast at several, say h, periods into the future, a modeller faces a choice between iterating one-step-ahead forecasts (the IMS technique), or directly modeling the relationship between observations separated by an h-period interval and using it for forecasting (DMS forecasting). It is known that structural breaks, unit-root non-stationarity and residual autocorrelation may improve DMS accuracy in finite samples, all of which occur when modelling the South African GDP over the period 1965–2000. This paper analyzes the forecasting properties of 779 multivariate and univariate models that combine different techniques of robust forecasting. We find strong evidence supporting the use of DMS and intercept correction, and attribute their superior forecasting performance to their robustness in the presence of breaks. 相似文献
9.
We provide probabilistic forecasts of photovoltaic (PV) production, for several PV plants located in France up to 6 days of lead time, with a 30-min timestep. First, we derive multiple forecasts from numerical weather predictions (ECMWF and Météo France), including ensemble forecasts. Second, our parameter-free online learning technique generates a weighted combination of the production forecasts for each PV plant. The weights are computed sequentially before each forecast using only past information. Our strategy is to minimize the Continuous Ranked Probability Score (CRPS). We show that our technique provides forecast improvements for both deterministic and probabilistic evaluation tools. 相似文献
10.
《International Journal of Forecasting》2022,38(4):1492-1499
The M5 accuracy competition has presented a large-scale hierarchical forecasting problem in a realistic grocery retail setting in order to evaluate an extended range of forecasting methods, particularly those adopting machine learning. The top ranking solutions adopted a global bottom-up approach, by which is meant using global forecasting methods to generate bottom level forecasts in the hierarchy and then using a bottom-up strategy to obtain coherent forecasts for aggregate levels. However, whether the observed superior performance of the global bottom-up approach is robust over various test periods or only an accidental result, is an important question for retail forecasting researchers and practitioners. We conduct experiments to explore the robustness of the global bottom-up approach, and make comments on the efforts made by the top-ranking teams to improve the core approach. We find that the top-ranking global bottom-up approaches lack robustness across time periods in the M5 data. This inconsistent performance makes the M5 final rankings somewhat of a lottery. In future forecasting competitions, we suggest the use of multiple rolling test sets to evaluate the forecasting performance in order to reward robustly performing forecasting methods, a much needed characteristic in any application. 相似文献
11.
《International Journal of Forecasting》2022,38(4):1448-1459
In this study, we addressed the problem of point and probabilistic forecasting by describing a blending methodology for machine learning models from the gradient boosted trees and neural networks families. These principles were successfully applied in the recent M5 Competition in both the Accuracy and Uncertainty tracks. The key points of our methodology are: (a) transforming the task into regression on sales for a single day; (b) information-rich feature engineering; (c) creating a diverse set of state-of-the-art machine learning models; and (d) carefully constructing validation sets for model tuning. We show that the diversity of the machine learning models and careful selection of validation examples are most important for the effectiveness of our approach. Forecasting data have an inherent hierarchical structure (12 levels) but none of our proposed solutions exploited the hierarchical scheme. Using the proposed methodology, we ranked within the gold medal range in the Accuracy track and within the prizes in the Uncertainty track. Inference code with pre-trained models are available on GitHub.1 相似文献
12.
Learning cycles in Bertrand competition with differentiated commodities and competing learning rules
Mikhail Anufriev Dávid Kopányi Jan Tuinstra 《Journal of Economic Dynamics and Control》2013,37(12):2562-2581
This paper stresses the importance of heterogeneity in learning. We consider a Bertrand oligopoly with firms using either least squares learning or gradient learning for determining the price. We demonstrate that convergence properties of the rules are strongly affected by heterogeneity. In particular, gradient learning may become unstable as the number of gradient learners increases. Endogenous choice between the learning rules may induce cyclical switching. Stable gradient learning gives higher average profit than least squares learning, making firms switch to gradient learning. This can destabilize gradient learning which, because of decreasing profits, makes firms switch back to least squares learning. 相似文献
13.
《International Journal of Forecasting》2019,35(2):741-755
The introduction of artificial intelligence has given us the ability to build predictive systems with unprecedented accuracy. Machine learning is being used in virtually all areas in one way or another, due to its extreme effectiveness. One such area where predictive systems have gained a lot of popularity is the prediction of football match results. This paper demonstrates our work on the building of a generalized predictive model for predicting the results of the English Premier League. Using feature engineering and exploratory data analysis, we create a feature set for determining the most important factors for predicting the results of a football match, and consequently create a highly accurate predictive system using machine learning. We demonstrate the strong dependence of our models’ performances on important features. Our best model using gradient boosting achieved a performance of 0.2156 on the ranked probability score (RPS) metric for game weeks 6 to 38 for the English Premier League aggregated over two seasons (2014–2015 and 2015–2016), whereas the betting organizations that we consider (Bet365 and Pinnacle Sports) obtained an RPS value of 0.2012 for the same period. Since a lower RPS value represents a higher predictive accuracy, our model was not able to outperform the bookmaker’s predictions, despite obtaining promising results. 相似文献
14.
Umberto Amato Anestis Antoniadis Italia De Feis Yannig Goude Audrey Lagache 《International Journal of Forecasting》2021,37(1):171-185
Short-Term Load Forecasting (STLF) is a fundamental instrument in the efficient operational management and planning of electric utilities. Emerging smart grid technologies pose new challenges and opportunities. Although load forecasting at the aggregate level has been extensively studied, electrical load forecasting at fine-grained geographical scales of households is more challenging. Among existing approaches, semi-parametric generalized additive models (GAM) have been increasingly popular due to their accuracy, flexibility, and interpretability. Their applicability is justified when forecasting is addressed at higher levels of aggregation, since the aggregated load pattern contains relatively smooth additive components. High resolution data are highly volatile, forecasting the average load using GAM models with smooth components does not provide meaningful information about the future demand. Instead, we need to incorporate irregular and volatile effects to enhance the forecast accuracy. We focus on the analysis of such hybrid additive models applied on smart meters data and show that it leads to improvement of the forecasting performances of classical additive models at low aggregation levels. 相似文献
15.
《International Journal of Forecasting》2023,39(1):244-265
As the penetration of solar energy generation into power systems keeps rising, intra-hour solar forecasting (IHSF) is becoming increasingly important for the secure and economical operation of a power system. One major difficulty in providing very accurate IHSF emanates from rapid cloud changes in the sky. The ground-based sky image (GSI) provides the intuitive information of intra-hour cloud changes and has thus been widely utilized in studies on IHSF. This paper presents a systematic review of the state-of-the-art of ground-based sky image-based intra-hour solar forecasting (GSI-IHSF). To our knowledge, we first propose a generic framework of GSI-IHSF consisting of four modules, i.e., sky image acquisition, sky image preprocessing, cloud forecasting, and solar forecasting. Then, as for each module, this paper introduces its core function, shows the major challenges, briefly reviews several extensively used techniques, summarizing research trends. Finally, this paper offers a prospect of GSI-IHSF research, discusses recent advances that demonstrate the potential for a great improvement in forecast accuracy, pointing out some new requirements and challenges that should be further investigated in the future. 相似文献
16.
《International Journal of Forecasting》2020,36(4):1260-1289
This study uses innovative tools recently proposed in the statistical learning literature to assess the capability of standard exchange rate models to predict the exchange rate in the short and long runs. Our results show that statistical learning methods deliver remarkably good performance, outperforming the random walk in forecasting the exchange rate at different forecasting horizons, with the exception of the very short term (a period of one to two months). These results were robust across countries, time, and models. We then used these tools to compare the predictive capabilities of different exchange rate models and model specifications, and found that sticky price versions of the monetary model with an error correction specification delivered the best performance. We also explain the operation of the statistical learning models by developing measures of variable importance and analyzing the kind of relationship that links each variable with the outcome. This gives us a better understanding of the relationship between the exchange rate and economic fundamentals, which appears complex and characterized by strong non-linearities. 相似文献
17.
This paper suggests a novel inhomogeneous Markov switching approach for the probabilistic forecasting of industrial companies’ electricity loads, for which the load switches at random times between production and standby regimes. The model that we propose describes the transitions between the regimes using a hidden Markov chain with time-varying transition probabilities that depend on calendar variables. We model the demand during the production regime using an autoregressive moving-average (ARMA) process with seasonal patterns, whereas we use a much simpler model for the standby regime in order to reduce the complexity. The maximum likelihood estimation of the parameters is implemented using a differential evolution algorithm. Using the continuous ranked probability score (CRPS) to evaluate the goodness-of-fit of our model for probabilistic forecasting, it is shown that this model often outperforms classical additive time series models, as well as homogeneous Markov switching models. We also propose a simple procedure for classifying load profiles into those with and without regime-switching behaviors. 相似文献
18.
《International Journal of Forecasting》2019,35(2):776-782
How can you tell whether a particular sports dataset really adds value, particularly with regard to betting effectiveness? The method introduced in this paper provides a way for any analyst in almost any sport to attempt to determine the additional value of almost any dataset. It relies on the use of deep learning, comprehensive historical box score statistics, and the existence of betting markets. When the method is applied as an illustration to a novel dataset for the NBA, it is shown to provide more information than regular box score statistics alone, and appears to generate above-breakeven wagering profits. 相似文献
19.
《International Journal of Forecasting》2022,38(4):1405-1414
Hierarchical forecasting with intermittent time series is a challenge in both research and empirical studies. Extensive research focuses on improving the accuracy of each hierarchy, especially the intermittent time series at bottom levels. Then, hierarchical reconciliation can be used to improve the overall performance further. In this paper, we present a hierarchical-forecasting-with-alignment approach that treats the bottom-level forecasts as mutable to ensure higher forecasting accuracy on the upper levels of the hierarchy. We employ a pure deep learning forecasting approach, N-BEATS, for continuous time series at the top levels, and a widely used tree-based algorithm, LightGBM, for intermittent time series at the bottom level. The hierarchical-forecasting-with-alignment approach is a simple yet effective variant of the bottom-up method, accounting for biases that are difficult to observe at the bottom level. It allows suboptimal forecasts at the lower level to retain a higher overall performance. The approach in this empirical study was developed by the first author during the M5 Accuracy competition, ranking second place. The method is also business orientated and can be used to facilitate strategic business planning. 相似文献
20.
Adam Richardson Thomas van Florenstein Mulder Tuğrul Vehbi 《International Journal of Forecasting》2021,37(2):941-948
Can machine-learning algorithms help central banks understand the current state of the economy? Our results say yes! We contribute to the emerging literature on forecasting macroeconomic variables using machine-learning algorithms by testing the nowcast performance of common algorithms in a full ‘real-time’ setting—that is, with real-time vintages of New Zealand GDP growth (our target variable) and real-time vintages of around 600 predictors. Our results show that machine-learning algorithms are able to significantly improve over a simple autoregressive benchmark and a dynamic factor model. We also show that machine-learning algorithms have the potential to add value to, and in one case improve on, the official forecasts of the Reserve Bank of New Zealand. 相似文献