This paper analyzes the inflation forecast errors over the period 2021Q1–2022Q3 using forecasts of core and headline inflation from the International Monetary Fund World Economic Outlook for a large group of advanced and emerging market economies. The findings reveal evidence of forecast bias that worsened initially and then subsided towards the end of the sample. There is also evidence of forecast oversmoothing, indicating rigidity in forecast revision in the face of incoming information. Focusing on core inflation forecast errors in 2021, four factors provide a potential ex post explanation: a stronger-than-anticipated demand recovery; demand-induced pressures on supply chains; the demand shift from services to goods at the onset of the pandemic; and labor market tightness. Ex ante, we find that the size of the COVID-19 fiscal stimulus packages announced by different governments in 2020 correlates positively with core inflation forecast errors in advanced economies. This result hints at potential forecast inefficiency, but we caution that it hinges on the outcomes of a few, albeit large, economies.
{"title":"How we missed the inflation surge: An anatomy of post-2020 inflation forecast errors","authors":"Christoffer Koch, Diaa Noureldin","doi":"10.1002/for.3088","DOIUrl":"10.1002/for.3088","url":null,"abstract":"<p>This paper analyzes the inflation forecast errors over the period 2021Q1–2022Q3 using forecasts of core and headline inflation from the International Monetary Fund <i>World Economic Outlook</i> for a large group of advanced and emerging market economies. The findings reveal evidence of forecast bias that worsened initially and then subsided towards the end of the sample. There is also evidence of forecast oversmoothing, indicating rigidity in forecast revision in the face of incoming information. Focusing on core inflation forecast errors in 2021, four factors provide a potential ex post explanation: a stronger-than-anticipated demand recovery; demand-induced pressures on supply chains; the demand shift from services to goods at the onset of the pandemic; and labor market tightness. Ex ante, we find that the size of the COVID-19 fiscal stimulus packages announced by different governments in 2020 correlates positively with core inflation forecast errors in advanced economies. This result hints at potential forecast inefficiency, but we caution that it hinges on the outcomes of a few, albeit large, economies.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140154734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wind power has emerged as a successful component within power systems. The ability to reliably and accurately forecast wind speed is of great importance in maintaining the security and stability of the power grid. However, the significance of explaining prediction models has often been overlooked by researchers. To address this gap, this study introduces a novel approach to wind speed forecasting that incorporates a significant decomposition method, attention-based machine learning, and local explanation techniques. The proposed model utilizes grid search variational mode decomposition to decompose the wind speed sequence into different modes while employing gate recurrent unit with an attention mechanism to achieve superior forecasting performance. Experimental evaluations conducted on eight real-world wind speed datasets demonstrate that the proposed approach outperforms other popular models across multiple performance criteria. In two specific experiments, the proposed approach achieved a minimal mean absolute percentage error of 2.74% and 1.70%, respectively. Furthermore, local interpretable model-agnostic explanations (LIME) were employed to assess the influence of factors, highlighting whether they positively or negatively affected the predicted values.
{"title":"Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model-agnostic explanations for multivariate wind speed forecasting","authors":"Lu Peng, Sheng-Xiang Lv, Lin Wang","doi":"10.1002/for.3097","DOIUrl":"10.1002/for.3097","url":null,"abstract":"<p>Wind power has emerged as a successful component within power systems. The ability to reliably and accurately forecast wind speed is of great importance in maintaining the security and stability of the power grid. However, the significance of explaining prediction models has often been overlooked by researchers. To address this gap, this study introduces a novel approach to wind speed forecasting that incorporates a significant decomposition method, attention-based machine learning, and local explanation techniques. The proposed model utilizes grid search variational mode decomposition to decompose the wind speed sequence into different modes while employing gate recurrent unit with an attention mechanism to achieve superior forecasting performance. Experimental evaluations conducted on eight real-world wind speed datasets demonstrate that the proposed approach outperforms other popular models across multiple performance criteria. In two specific experiments, the proposed approach achieved a minimal mean absolute percentage error of 2.74% and 1.70%, respectively. Furthermore, local interpretable model-agnostic explanations (LIME) were employed to assess the influence of factors, highlighting whether they positively or negatively affected the predicted values.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140108185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Martin Feldkircher, Luis Gruber, Florian Huber, Gregor Kastner
We assess the relationship between model size and complexity in the time-varying parameter vector autoregression (VAR) framework via thorough predictive exercises for the euro area, the United Kingdom, and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets, while simpler models tend to perform better in sizeable data sets. To combine the best of both worlds, novel shrinkage priors help to mitigate the curse of dimensionality, resulting in competitive forecasts for all scenarios considered. Furthermore, we discuss dynamic model selection to improve upon the best performing individual model for each point in time.
{"title":"Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian vector autoregressions?","authors":"Martin Feldkircher, Luis Gruber, Florian Huber, Gregor Kastner","doi":"10.1002/for.3121","DOIUrl":"10.1002/for.3121","url":null,"abstract":"<p>We assess the relationship between model size and complexity in the time-varying parameter vector autoregression (VAR) framework via thorough predictive exercises for the euro area, the United Kingdom, and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets, while simpler models tend to perform better in sizeable data sets. To combine the best of both worlds, novel shrinkage priors help to mitigate the curse of dimensionality, resulting in competitive forecasts for all scenarios considered. Furthermore, we discuss dynamic model selection to improve upon the best performing individual model for each point in time.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140108189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matteo Bonato, Oguzhan Cepni, Rangan Gupta, Christian Pierdzioch
We analyze the out-of-sample predictive power of sentiment for the realized volatility of agricultural commodity price returns. We use high-frequency intra-day data covering the period from 2009 to 2020 to estimate realized volatility. Our baseline forecasting model is a heterogeneous autoregressive (HAR) model, which we extend to include sentiment. We further enhance this model by incorporating various key realized moments such as leverage, realized skewness, realized kurtosis, realized upside (“good”) volatility, realized downside (“bad”) volatility, realized jumps, realized upside tail risk, and realized downside tail risk. In order to setup a forecasting model, we use (i) forward and backward stepwise predictor selection and (ii) a model-based averaging algorithm. The forecasting models constructed through these algorithms outperform both the baseline HAR-RV model and the HAR-RV-sentiment model. We conclude that, for the agricultural commodities studied in our research, realized moments play a more significant role in forecasting realized volatility compared to sentiment.
{"title":"Forecasting the realized volatility of agricultural commodity prices: Does sentiment matter?","authors":"Matteo Bonato, Oguzhan Cepni, Rangan Gupta, Christian Pierdzioch","doi":"10.1002/for.3106","DOIUrl":"10.1002/for.3106","url":null,"abstract":"<p>We analyze the out-of-sample predictive power of sentiment for the realized volatility of agricultural commodity price returns. We use high-frequency intra-day data covering the period from 2009 to 2020 to estimate realized volatility. Our baseline forecasting model is a heterogeneous autoregressive (HAR) model, which we extend to include sentiment. We further enhance this model by incorporating various key realized moments such as leverage, realized skewness, realized kurtosis, realized upside (“good”) volatility, realized downside (“bad”) volatility, realized jumps, realized upside tail risk, and realized downside tail risk. In order to setup a forecasting model, we use (i) forward and backward stepwise predictor selection and (ii) a model-based averaging algorithm. The forecasting models constructed through these algorithms outperform both the baseline HAR-RV model and the HAR-RV-sentiment model. We conclude that, for the agricultural commodities studied in our research, realized moments play a more significant role in forecasting realized volatility compared to sentiment.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140108166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The accurate forecasting of agricultural futures prices is critical for ensuring national food security. Therefore, this study proposes a text-based deep learning forecasting model. This model first uses the ChineseBERT + a text convolution neural network to classify Weibo text and obtain a raw sentiment index. Then, complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, correlation coefficient, and sample entropy are combined to decompose and reconstruct the raw sentiment index and obtain a denoised sentiment index. Subsequently, the neural basis expansion analysis with exogenous variables is improved by designing a weight coefficient and Optuna is used to optimize the designed weight coefficient and the hyperparameters. Finally, the SHapley Additive exPlanations value is used to increase the interpretability of prediction results. Corn futures prices for the Dalian Exchange are used in forecasting to validate the accuracy and stability of the proposed model. Experimental results show that the proposed denoising sentiment index contributes more to the improvement of predictive model performance than the raw sentiment index. The proposed text-based deep predictive model demonstrates strong predictive ability for prediction horizons of 30 and 60 days. SHapley Additive exPlanations value analysis shows that the three features with greater effects on corn futures prices are as follows: “Corn Spot Price of Zhengzhou market,” “CBOT_corn_futures_price,” and “Pork futures price.”
{"title":"Text-based corn futures price forecasting using improved neural basis expansion network","authors":"Lin Wang, Wuyue An, Feng-Ting Li","doi":"10.1002/for.3119","DOIUrl":"10.1002/for.3119","url":null,"abstract":"<p>The accurate forecasting of agricultural futures prices is critical for ensuring national food security. Therefore, this study proposes a text-based deep learning forecasting model. This model first uses the ChineseBERT + a text convolution neural network to classify Weibo text and obtain a raw sentiment index. Then, complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, correlation coefficient, and sample entropy are combined to decompose and reconstruct the raw sentiment index and obtain a denoised sentiment index. Subsequently, the neural basis expansion analysis with exogenous variables is improved by designing a weight coefficient and Optuna is used to optimize the designed weight coefficient and the hyperparameters. Finally, the SHapley Additive exPlanations value is used to increase the interpretability of prediction results. Corn futures prices for the Dalian Exchange are used in forecasting to validate the accuracy and stability of the proposed model. Experimental results show that the proposed denoising sentiment index contributes more to the improvement of predictive model performance than the raw sentiment index. The proposed text-based deep predictive model demonstrates strong predictive ability for prediction horizons of 30 and 60 days. SHapley Additive exPlanations value analysis shows that the three features with greater effects on corn futures prices are as follows: “Corn Spot Price of Zhengzhou market,” “CBOT_corn_futures_price,” and “Pork futures price.”</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140072081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The reformed Basel framework has left value at risk (VaR) as a basic tool of validating risk models. Within this framework, VaR independence tests have been regarded as critical to ensuring stability during periods of financial turmoil. However, until now, there is no consent among researchers regarding the choice of the appropriate test. The available procedures are either inaccurate in finite samples or need to rely on Monte Carlo simulations. To remedy these problems, we propose a new method for testing VaR models, based on the distribution of the number of runs. It outperforms the existing methods in two main aspects: First, it is exact in finite samples and thus allows for perfect control over the Type 1 error; second, its distribution is available in a closed form, so it does not require simulations before implementation. We show that it is the most adequate current procedure for testing low-level VaR series, which corresponds to today's regulatory standards.
改革后的巴塞尔框架将风险价值(VaR)作为验证风险模型的基本工具。在这一框架内,风险价值独立性测试被认为是确保金融动荡时期稳定的关键。然而,直到现在,研究人员对选择适当的测试方法还没有达成一致意见。现有的程序要么在有限样本中不准确,要么需要依赖蒙特卡罗模拟。为了解决这些问题,我们提出了一种基于运行次数分布的 VaR 模型检验新方法。它在两个主要方面优于现有方法:首先,它在有限样本中是精确的,因此可以完美地控制第一类误差;其次,它的分布以封闭形式存在,因此在实施前无需进行模拟。我们证明,它是目前测试低水平 VaR 系列的最适当程序,符合当今的监管标准。
{"title":"New runs-based approach to testing value at risk forecasts","authors":"Marta Małecka","doi":"10.1002/for.3115","DOIUrl":"10.1002/for.3115","url":null,"abstract":"<p>The reformed Basel framework has left value at risk (VaR) as a basic tool of validating risk models. Within this framework, VaR independence tests have been regarded as critical to ensuring stability during periods of financial turmoil. However, until now, there is no consent among researchers regarding the choice of the appropriate test. The available procedures are either inaccurate in finite samples or need to rely on Monte Carlo simulations. To remedy these problems, we propose a new method for testing VaR models, based on the distribution of the number of runs. It outperforms the existing methods in two main aspects: First, it is exact in finite samples and thus allows for perfect control over the Type 1 error; second, its distribution is available in a closed form, so it does not require simulations before implementation. We show that it is the most adequate current procedure for testing low-level VaR series, which corresponds to today's regulatory standards.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140072122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Juanjuan Wang, Shujie Zhou, Wentong Liu, Lin Jiang
Electronic and digital trading models have made stock trading more accessible and convenient, leading to exponential growth in trading data. With a wealth of trading data available, researchers have found opportunities to extract valuable insights by uncovering patterns in stock price movements and market dynamics. Deep learning models are increasingly being employed for stock price prediction. While neural networks offer superior computational capabilities compared with traditional statistical methods, their results often lack interpretability, limiting their utility in explaining stock price volatility and investment behavior. To address this challenge, we propose a causality-based method that incorporates a multivariate approach, integrating news event attention sequences and sentiment index sequences. The goal is to capture the intricate and multifaceted relationships among news events, media sentiment, and stock prices. We illustrate the application of this proposed approach using a Global Database of Events, Language, and Tone global event database, demonstrating its benefits through the analysis of attention sequences and media sentiment index sequences for news events across various categories. This research not only identifies promising directions for further exploration but also offers insights with implications for informed investment decisions.
{"title":"An ensemble model for stock index prediction based on media attention and emotional causal inference","authors":"Juanjuan Wang, Shujie Zhou, Wentong Liu, Lin Jiang","doi":"10.1002/for.3108","DOIUrl":"10.1002/for.3108","url":null,"abstract":"<p>Electronic and digital trading models have made stock trading more accessible and convenient, leading to exponential growth in trading data. With a wealth of trading data available, researchers have found opportunities to extract valuable insights by uncovering patterns in stock price movements and market dynamics. Deep learning models are increasingly being employed for stock price prediction. While neural networks offer superior computational capabilities compared with traditional statistical methods, their results often lack interpretability, limiting their utility in explaining stock price volatility and investment behavior. To address this challenge, we propose a causality-based method that incorporates a multivariate approach, integrating news event attention sequences and sentiment index sequences. The goal is to capture the intricate and multifaceted relationships among news events, media sentiment, and stock prices. We illustrate the application of this proposed approach using a Global Database of Events, Language, and Tone global event database, demonstrating its benefits through the analysis of attention sequences and media sentiment index sequences for news events across various categories. This research not only identifies promising directions for further exploration but also offers insights with implications for informed investment decisions.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140072121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, the semiparametric methods for the joint estimation and prediction of value at risk (VaR) and expected shortfall (ES) have triggered great interests and attention. Compared to existing literature which usually incorporates realized volatility (RV) into the dynamic semiparametric risk models, this paper considers three more robust proxies (medRV, BPV, and RK) of intraday volatility in the models to verify whether high-frequency information can improve the joint prediction ability of risk measures. To strengthen the persuasion of conclusions, four international stock indices (S&P500, Nikkei225, GDAXI, and DJIA) are applied to these models to estimate and forecast VaR and ES at different probability levels (1%, 2.5%, 5%, and 10%). Then, the predicted VaR and ES are backtested by several methods individually, and the popular score function FZ0 and MCS test are used to compare the effects of jointly predicting risk measures. Our results confirm that these semiparametric models containing intraday information outperform the benchmark models for four stocks and various probability levels, and medRV is the best volatility measure in improving the effects of models.
近年来,用于联合估计和预测风险价值(VaR)和预期缺口(ES)的半参数方法引发了人们的极大兴趣和关注。与现有文献通常将已实现波动率(RV)纳入动态半参数风险模型相比,本文在模型中考虑了三种更稳健的日内波动率替代指标(medRV、BPV 和 RK),以验证高频信息是否能提高风险度量的联合预测能力。为了加强结论的说服力,将四个国际股票指数(S&P500、日经 225、GDAXI 和道琼斯工业平均指数)应用于这些模型,以估计和预测不同概率水平(1%、2.5%、5% 和 10%)的 VaR 和 ES。然后,用几种方法分别对预测的 VaR 和 ES 进行回溯测试,并使用流行的评分函数 FZ0 和 MCS 测试来比较联合预测风险度量的效果。我们的结果证实,对于四种股票和各种概率水平,这些包含盘中信息的半参数模型优于基准模型,而 medRV 是改善模型效果的最佳波动率指标。
{"title":"Can intraday data improve the joint estimation and prediction of risk measures? Evidence from a variety of realized measures","authors":"Zhimin Wu, Guanghui Cai","doi":"10.1002/for.3111","DOIUrl":"10.1002/for.3111","url":null,"abstract":"<p>In recent years, the semiparametric methods for the joint estimation and prediction of value at risk (VaR) and expected shortfall (ES) have triggered great interests and attention. Compared to existing literature which usually incorporates realized volatility (RV) into the dynamic semiparametric risk models, this paper considers three more robust proxies (medRV, BPV, and RK) of intraday volatility in the models to verify whether high-frequency information can improve the joint prediction ability of risk measures. To strengthen the persuasion of conclusions, four international stock indices (S&P500, Nikkei225, GDAXI, and DJIA) are applied to these models to estimate and forecast VaR and ES at different probability levels (1%, 2.5%, 5%, and 10%). Then, the predicted VaR and ES are backtested by several methods individually, and the popular score function FZ0 and MCS test are used to compare the effects of jointly predicting risk measures. Our results confirm that these semiparametric models containing intraday information outperform the benchmark models for four stocks and various probability levels, and medRV is the best volatility measure in improving the effects of models.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140072096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents a novel and fully probabilistic approach for combining model-based forecasts with surveys or other judgmental forecasts. In our method, survey forecasts are integrated as penalty terms for the model parameters, facilitating a probabilistic exploration of additional insights obtained from surveys. We apply this approach to estimate a growth-at-risk model for real GDP growth in the United States. The results reveal that this additional shrinkage significantly improves prediction performance, with the information from surveys even exerting an influence on the lower tails of the distribution.
本研究提出了一种新颖的全概率方法,用于将基于模型的预测与调查或其他判断性预测相结合。在我们的方法中,调查预测被整合为模型参数的惩罚项,从而促进了对从调查中获得的额外见解的概率探索。我们将这种方法用于估算美国实际 GDP 增长的风险增长模型。结果表明,这种额外的缩减显著提高了预测性能,来自调查的信息甚至对分布的低尾部产生了影响。
{"title":"Disciplining growth-at-risk models with survey of professional forecasters and Bayesian quantile regression","authors":"Milan Szabo","doi":"10.1002/for.3120","DOIUrl":"10.1002/for.3120","url":null,"abstract":"<p>This study presents a novel and fully probabilistic approach for combining model-based forecasts with surveys or other judgmental forecasts. In our method, survey forecasts are integrated as penalty terms for the model parameters, facilitating a probabilistic exploration of additional insights obtained from surveys. We apply this approach to estimate a growth-at-risk model for real GDP growth in the United States. The results reveal that this additional shrinkage significantly improves prediction performance, with the information from surveys even exerting an influence on the lower tails of the distribution.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140072085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geri Skenderi, Christian Joppi, Matteo Denitto, Marco Cristani
New fashion product sales forecasting is a challenging problem that involves many business dynamics and cannot be solved by classical forecasting approaches. In this paper, we investigate the effectiveness of systematically probing exogenous knowledge in the form of Google Trends time series and combining it with multi-modal information related to a brand-new fashion item, in order to effectively forecast its sales despite the lack of past data. In particular, we propose a neural network-based approach, where an encoder learns a representation of the exogenous time series, while the decoder forecasts the sales based on the Google Trends encoding and the available visual and metadata information. Our model works in a non-autoregressive manner, avoiding the compounding effect of large first-step errors. As a second contribution, we present VISUELLE, a publicly available dataset for the task of new fashion product sales forecasting, containing multimodal information for 5,577 real, new products sold between 2016 and 2019 from Nunalie, an Italian fast-fashion company. The dataset is equipped with images of products, metadata, related sales, and associated Google Trends. We use VISUELLE to compare our approach against state-of-the-art alternatives and several baselines, showing that our neural network-based approach is the most accurate in terms of both percentage and absolute error. It is worth noting that the addition of exogenous knowledge boosts the forecasting accuracy by 1.5% in terms of Weighted Absolute Percentage Error (WAPE), revealing the importance of exploiting informative external information. The code and dataset are both available online (at https://github.com/HumaticsLAB/GTM-Transformer).
{"title":"Well googled is half done: Multimodal forecasting of new fashion product sales with image-based google trends","authors":"Geri Skenderi, Christian Joppi, Matteo Denitto, Marco Cristani","doi":"10.1002/for.3104","DOIUrl":"10.1002/for.3104","url":null,"abstract":"<p>New fashion product sales forecasting is a challenging problem that involves many business dynamics and cannot be solved by classical forecasting approaches. In this paper, we investigate the effectiveness of systematically probing exogenous knowledge in the form of Google Trends time series and combining it with multi-modal information related to a brand-new fashion item, in order to effectively forecast its sales despite the lack of past data. In particular, we propose a neural network-based approach, where an encoder learns a representation of the exogenous time series, while the decoder forecasts the sales based on the Google Trends encoding and the available visual and metadata information. Our model works in a non-autoregressive manner, avoiding the compounding effect of large first-step errors. As a second contribution, we present VISUELLE, a publicly available dataset for the task of new fashion product sales forecasting, containing multimodal information for 5,577 real, new products sold between 2016 and 2019 from Nunalie, an Italian fast-fashion company. The dataset is equipped with images of products, metadata, related sales, and associated Google Trends. We use VISUELLE to compare our approach against state-of-the-art alternatives and several baselines, showing that our neural network-based approach is the most accurate in terms of both percentage and absolute error. It is worth noting that the addition of exogenous knowledge boosts the forecasting accuracy by 1.5% in terms of Weighted Absolute Percentage Error (WAPE), revealing the importance of exploiting informative external information. The code and dataset are both available online (at https://github.com/HumaticsLAB/GTM-Transformer).</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140071948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}