International Journal of Forecasting
Papers
2760
Papers 2827
1 page of 283 pages (2,827 results)
Newest
#1Adrian E. Raftery (UW: University of Washington)H-Index: 100
#2Hana Ševčíková (UW: University of Washington)H-Index: 16
Abstract null null Population forecasts are used by governments and the private sector for planning, with horizons up to about three generations (around 2100) for different purposes. The traditional methods are deterministic using scenarios, but probabilistic forecasts are desired to get an idea of accuracy, assess changes, and make decisions involving risks. In a significant breakthrough, since 2015, the United Nations has issued probabilistic population forecasts for all countries using a Baye...
Source
#1Michael Ellington (University of Liverpool)H-Index: 3
#2Xi Fu (University of Liverpool)H-Index: 3
Last. Yunyi Zhu (University of Liverpool)
view all 3 authors...
Abstract null null This paper proposes two new measures of illiquidity for real estate markets, utilising concepts from asset pricing. Segregating real estate through a regional lens, we provide an in-depth analysis of real estate returns and illiquidity for the US and UK. Our results provide statistically significant and economically meaningful evidence that real estate illiquidity predicts real estate returns out-of-sample over and above a variety of control variables.
Source
Abstract null null One of the most successful forecasting machine learning (ML) procedures is random forest (RF). In this paper, we propose a new mixed RF approach for modeling departures from linearity that helps identify (i) explanatory variables with nonlinear impacts, (ii) threshold values, and (iii) the closest parametric approximation. The methodology is applied to weekly forecasts of gasoline prices, cointegrated with international oil prices and exchange rates. Recent specifications for ...
1 CitationsSource
#1Erin Coughlan de Perez (Columbia University)H-Index: 14
#2Elisabeth Stephens (University of Reading)H-Index: 16
Last. Rachel Lowe (Lond: University of London)H-Index: 22
view all 9 authors...
Weather forecasts, climate change projections, and epidemiological predictions all represent domains that are using forecast data to take early action for risk management. However, the methods and applications of the modeling efforts in each of these three fields have been developed and applied with little cross-fertilization. This perspective identifies best practices in each domain that can be adopted by the others, which can be used to inform each field separately as well as to facilitate a m...
Source
#1Spyros Makridakis (University of Nicosia)H-Index: 52
#2Evangelos Spiliotis (NTUA: National Technical University of Athens)H-Index: 10
Last. Vassilios Assimakopoulos (NTUA: National Technical University of Athens)H-Index: 13
view all 3 authors...
Abstract null null The M5 competition follows the previous four M competitions, whose purpose is to learn from empirical evidence how to improve forecasting performance and advance the theory and practice of forecasting. M5 focused on a retail sales forecasting application with the objective to produce the most accurate point forecasts for 42,840 time series that represent the hierarchical unit sales of the largest retail company in the world, Walmart, as well as to provide the most accurate est...
1 CitationsSource
#1Nikola Gradojevic (U of G: University of Guelph)H-Index: 16
#2Dragan Kukolj (University of Novi Sad)H-Index: 18
Last. Vladimir Djakovic (University of Novi Sad)H-Index: 4
view all 4 authors...
Abstract null null This paper uses data sampled at hourly and daily frequencies to predict Bitcoin returns. We consider various advanced non-linear models based on a multitude of popular technical indicators that represent market trend, momentum, volume, and sentiment. We run a robust empirical exercise to observe the impact of forecast horizon, model type, time period, and the choice of inputs (predictors) on the forecast performance of the competing models. We find that Bitcoin prices are weak...
Source
#1Cuiqing Jiang (Hefei University of Technology)H-Index: 11
#2Lyu Ximei (Hefei University of Technology)
Last. Yong Ding (Hefei University of Technology)H-Index: 6
view all 5 authors...
Abstract null null It is difficult to predict the financial distress of unlisted public firms due to their longer disclosure cycle of accounting information and more inadequate continuity of market trading information compared to listed firms. In this paper, we propose a framework to predict the financial distress of unlisted public firms using current reports. Specifically, to better represent the meaning of current report texts, we propose a semantic feature extraction method based on a word e...
Source
#1Chuan Zhang (Northeastern University (China))
#2Yu-Xin Tian (Northeastern University (China))
Last. Zhi-Ping Fan (Northeastern University (China))H-Index: 45
view all 3 authors...
Abstract null null Traditional sales forecasting methods are mainly based on historical sales data, which result in a certain lag. The relationship between sales volume and its influencing factors is intricate and often non-linear. In view of this, we propose a novel product forecasting method using online reviews and search engine data. Firstly, a dictionary-based sentiment analysis method is developed to convert the textual review concerning each attribute of the product into the corresponding...
Source
#1Xueping Tan (SJTU: Shanghai Jiao Tong University)
#2Kavita Sirichand (Lboro: Loughborough University)H-Index: 5
Last. Xinyu Wang (CUMT: China University of Mining and Technology)H-Index: 1
view all 4 authors...
Abstract null null Using a broad selection of 53 carbon (EUA) related, commodity and financial predictors, we provide a comprehensive assessment of the out-of-sample (OOS) predictability of weekly European carbon futures return. We assess forecast performance using both statistical and economic value metrics over an OOS period spanning from January 2013 to May 2018. Two main types of dimension reduction techniques are employed: (i) shrinkage of coefficient estimates and (ii) factor models. We fi...
Source
Abstract null null The ability to forecast the concentration of air pollutants in an urban region is crucial for decision-makers wishing to reduce the impact of pollution on public health through active measures (e.g. temporary traffic closures). In this study, we present a machine learning approach applied to forecasts of the day-ahead maximum value of ozone concentration for several geographical locations in southern Switzerland. Due to the low density of measurement stations and to the comple...
Source
12345678910
Top fields of study
Statistics
Financial economics
Consensus forecast
Econometrics
Autoregressive model
Economics
Mathematics
Computer science