Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak

Epidemic is a rapid and wide spread of infectious disease threatening many lives and economy damages. It is important to fore-tell the epidemic lifetime so to decide on timely and remedic actions. These measures include closing borders, schools, suspending community services and commuters. Resuming such curfews depends on the momentum of the outbreak and its rate of decay. Being able to accurately forecast the fate of an epidemic is an extremely important but difficult task. Due to limited knowledge of the novel disease, the high uncertainty involved and the complex societal-political factors that influence the widespread of the new virus, any forecast is anything but reliable. Another factor is the insufficient amount of available data. Data samples are often scarce when an epidemic just started. With only few training samples on hand, finding a forecasting model which offers forecast at the best efforts is a big challenge in machine learning. In the past, three popular methods have been proposed, they include 1) augmenting the existing little data, 2) using a panel selection to pick the best forecasting model from several models, and 3) fine-tuning the parameters of an individual forecasting model for the highest possible accuracy. In this paper, a methodology that embraces these three virtues of data mining from a small dataset is proposed. An experiment that is based on the recent coronavirus outbreak originated from Wuhan is conducted by applying this methodology. It is shown that an optimized forecasting model that is constructed from a new algorithm, namely polynomial neural network with corrective feedback (PNN+cf) is able to make a forecast that has relatively the lowest prediction error. The results showcase that the newly proposed methodology and PNN+cf are useful in generating acceptable forecast upon the critical time of disease outbreak when the samples are far from abundant

地域と世界的気候様相を組み合わせた集中豪雨予想法とそのマレーシア地方への適応

Successive days of precipitation are known to cause flooding in monsoon-susceptible countries.The analysis of extreme precipitation trends is important for the prediction of high precipitation events. Forecasting of daily precipitation facilitates the prediction of the occurrences of rainfall and number of wet days. Using the maximum five-day accumulated precipitation (MX5d) index, we can predict the magnitude of precipitation in a specific period as it may indicate the extreme precipitation.Traditionally, a data-driven model is built on a whole data set describing the phenomenon within the data. This type of model does not considered the seasonal processes embedded in the data.Therefore, there is a need to built a model that encompassing different time scale and localized.One of the ways of doing this is to discover the different physically embedded relationships in precipitation process at different seasonal period and to built separate localized models for each of these seasonal periods. The use of committee models such as modular and ensemble models in weather and hydrological forecasting are increasing day by day. The study uses the modular concept by separating the heavy precipitation events based on sub-processes which are the seasonal monsoon and trained the subset of seasonal data using data driven models.Besides that, the study is carried out to evaluate the influence of global climate indices on local precipitation. It is interesting to see the influence of combining global and local predictors on local precipitation events. The method used past MX5d data and global climate indices such as Southern Oscillation Index (SOI), Madden Julian Oscillation (MJO), and Dipole Mode Index (DMI) in Kuantan and Kota Bharu, Malaysia using modular model trained on subset of data that represent the seasonal monsoon.The analysis started with evaluating the local and global inputs (MX5d with SOI, MJO, and DMI) in order to investigate the concurrent effect of lagged values of local precipitation data and global climate indices on seasonal extreme precipitation. Four subset of data are sampled representing two major seasonal variations in Malaysia. The experimental data are focused on the east coast area of Malaysia such that the effect of northeast monsoon season causes heavy precipitation events. The results showed that the combination of local and global modes in a modular model is favourable than a single localized mode. The proposed modular model is promising an encouraging result when different subset of data are trained on separate methods with different parameter values.九州工業大学博士学位論文 学位記番号:情工博甲第297号 学位授与年月日:平成27年3月25日1 Introduction|2 Literature Review|3 Methodology of the study|4 Data and application of methods|5 Results and discussions|6 Conclusion九州工業大学平成26年

Rare events forecasting using a residual-feedback GMDH neural network

The prediction of rare events is a pressing scientific problem. Events such as extreme meteorological conditions, may aggravate human morbidity and mortality. Yet, their prediction is inherently difficult as, by definition, these events are characterised by low occurrence, high sampling variation, and uncertainty. For example, earthquakes have a high magnitude variation and are irregular. In the past, many attempts have been made to predict rare events using linear time series forecasting algorithms, but these algorithms have failed to capture the surprise events. This study proposes a novel strategy that extends existing GMDH or polynomial neural network techniques. The new strategy, called residual-feedback, retains and reuses past prediction errors as part of the multivariate sample data that provides relevant multivariate inputs to the GMDH or polynomial neural networks. This is important because the strength of GMDH, like any neural network, is in predicting outcomes from multivariate data, and it is very noise-tolerant. GMDH is a well-known ensemble type of prediction method that is capable of modelling highly non-linear relations. It achieves optimal accuracy by testing all possible structures of polynomial forecasting models. The performance results of the GMDH alone, and the extended GMDH with residual-feedback are compared for two case studies, namely global earthquake prediction and precipitation forecast by ground ozone information. The results show that GMDH with residual-feedback always yields the lowest error. (as on publisher webpage

Rare events forecasting using a residual-feedback GMDH neural network

The prediction of rare events is a pressing scientific problem. Events such as extreme meteorological conditions, may aggravate human morbidity and mortality. Yet, their prediction is inherently difficult as, by definition, these events are characterised by low occurrence, high sampling variation, and uncertainty. For example, earthquakes have a high magnitude variation and are irregular. In the past, many attempts have been made to predict rare events using linear time series forecasting algorithms, but these algorithms have failed to capture the surprise events. This study proposes a novel strategy that extends existing GMDH or polynomial neural network techniques. The new strategy, called residual-feedback, retains and reuses past prediction errors as part of the multivariate sample data that provides relevant multivariate inputs to the GMDH or polynomial neural networks. This is important because the strength of GMDH, like any neural network, is in predicting outcomes from multivariate data, and it is very noise-tolerant. GMDH is a well-known ensemble type of prediction method that is capable of modelling highly non-linear relations. It achieves optimal accuracy by testing all possible structures of polynomial forecasting models. The performance results of the GMDH alone, and the extended GMDH with residual-feedback are compared for two case studies, namely global earthquake prediction and precipitation forecast by ground ozone information. The results show that GMDH with residual-feedback always yields the lowest error. (as on publisher webpage