Forecasting with Big Data: A Review <s> Forecasting with Big Data in Population Dynamics <s> In the competitive environments, in which all sorts of organisations move it is of utmost importance to have information about clients. Public databases offer information about households and families. However, the non-crossed and non-georeferenced format of these databases often makes it difficult to extract typologies and information. ::: ::: There are only two public databases from which to get information at the household or family level in Spain: Population and Housing Censuses, which provide aggregated and georeferenced information, and the Family Expenditure Surveys, which provide information on household consumption, both published by the National Statistics Institute. The two databases cannot be directly cross-referenced, because the Family Expenditure Surveys offer a detailed description of the families, whereas the Census provides the same data but aggregated without cross-references. ::: ::: In this paper, we define a procedure for cross-referencing these DBs and calculating the economic household indexes for Spanish censal sections that define the average quarterly economic behaviour of the households located in each censal section. The necessary analysis procedure is based on neural networks and provides an estimate of the trend in these indexes over a series of years. The procedure can be easily extrapolated to similar problems with official data sources from other countries. <s> BIB001 </s> Forecasting with Big Data: A Review <s> Forecasting with Big Data in Population Dynamics <s> Politicians, planners and social scientists have an increasing need for tools clarifying the spatial distribution of relevant features. Special interest is in predicting changes in a what-if analysis: what would happen if we change some features in a specific way. To predict future developments requires a statistical model with inherent modelling uncertainty. In this paper we investigate Bayesian models which on the one hand are able to represent complex relations between geo-referenced variables and on the other hand estimate the inherent uncertainty in predictions. For solution the models require Markov-Chain Monte Carlo techniques. <s> BIB002 </s> Forecasting with Big Data: A Review <s> Forecasting with Big Data in Population Dynamics <s> Outlier analysis in data mining is to find the deviation and abnormal data in the databases. By finding and explaining the outliers, we can apply them to detecting the business frauds effectively. This paper analyzes the common characteristics of fraudulent behavior of customers in telecom industry systematically. Based on the outlier-finding by clustering in data mining, we propose an effective solution to forecast the customers who are maliciously in arrears. Coupled with the actual application of forecasting the customers who are maliciously in arrears in telecom industry, we propose the specific method to forecast this kind of customers by using Kohonen neural network clustering algorithm. <s> BIB003 </s> Forecasting with Big Data: A Review <s> Forecasting with Big Data in Population Dynamics <s> We propose a boosting method, multivariate L2Boosting, for multivari- ate linear regression based on some squared error loss for multivariate data. It can be applied to multivariate linear regression with continuous responses and to vector autoregressive time series. We prove, for i.i.d. as well as time series data, that multivariate L2Boosting can consistently recover sparse high-dimensional mul- tivariate linear functions, even when the number of predictor variables pn and the dimension of the response qn grow almost exponentially with sample size n, pn = qn = O(exp(Cn 1 )) (0 < < 1, 0 < C <1), but the '1-norm of the true underlying function is nite. Our theory seems to be among the rst to address the issue of large dimension of the response variable; the relevance of such settings is briey outlined. We also identify empirically some cases where our multivari- ate L2Boosting is better than multiple application of univariate methods to single response components, thus demonstrating that the multivariate approach can be very useful. <s> BIB004 </s> Forecasting with Big Data: A Review <s> Forecasting with Big Data in Population Dynamics <s> Data mining is a general term which refers to a set of several different techniques. These techniques differ, but they all exploit the idea that existing data contain information that can be used in the future. With large datasets this information is often hidden, but data mining techniques can be used to distill or uncover it. Various techniques can be used to classify data into subsets, predict outcomes based on the data, cluster records into like subgroups, or assign propensity scores for some measure to records. Several data mining based applications have been or are currently being used in NASS, and others have potential future application. To date, the most widely used data mining technique in NASS is the classification or decision tree. Decision Trees A decision tree model is constructed by segmenting a dataset using a series of simple rules. Each rule assigns observations to a segment based on the value of one input variable. One rule is applied after another, resulting in a hierarchy of segments within segments. The rules are chosen to maximally separate the sub-segments with respect to a chosen target variable. Algorithms such as the chi-square automatic interaction detection (CHAID) algorithm can be used to determine how to split the segments. The hierarchy is called a tree, and each segment is called a node. The original segment contains the entire data set and is called the root node of the tree. A node with all its successors is termed a branch of the node that created it. The final nodes are called leaves. NASS used decision tree models for several applications in the 2007 Census of Agriculture. The following paragraphs describe these and other current and potential applications of decision trees in the Agency: Census Non-response Weighting. Decision tree models were used to divide the 2007 census records into response propensity groups representing weighting adjustment cells (Cecere, 2008). This approach has been used by other survey organizations to do post survey non-response weighting adjustments (for example, Cohen, DiGaetano and Goksel, <s> BIB005 </s> Forecasting with Big Data: A Review <s> Forecasting with Big Data in Population Dynamics <s> This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their NMSEs are 0.02314 and 0.15384 respectively. <s> BIB006 </s> Forecasting with Big Data: A Review <s> Forecasting with Big Data in Population Dynamics <s> In the age of big data, information mining technology has undergone tremendous change; traditional forecasting mining technology has not been able to solve the information mining problems under a large scale of data. this paper put forward a modeling mechanism of information analysis and mining under the age of big data, the modeling mechanism is, first, construct the model of task decomposition of information by MapReduce tool, then, make data preprocessing and mining operation according to the single task data sheet, use mathematical model, artificial intelligence and other methods to construct the new ideas of information analysis and data mining under the age of big data, finally, a case study presented to demonstrate the feasibility and rationality of our approach . DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3278 Full Text: PDF <s> BIB007 </s> Forecasting with Big Data: A Review <s> Forecasting with Big Data in Population Dynamics <s> Forecasting time series data is crucial in a number of domains such as supply chain management and display advertisement. In these areas, the time series data to forecast is typically organized along multiple dimensions leading to a high number of time series that need to be forecasted. Most current approaches focus only on selection and optimizing a forecast model for a single time series. In this paper, we explore how we can utilize time series at different dimensions to increase forecast accuracy and, optionally, reduce model maintenance overhead. Solving this problem is challenging due to the large space of possibilities and possible high model creation costs. We propose a model configuration advisor that automatically determines the best set of models, a model configuration, for a given multi-dimensional data set. Our approach is based on a general process that iteratively examines more and more models and simultaneously controls the search space depending on the data set, model type and available hardware. The final model configuration is integrated into F2DB, an extension of PostgreSQL, that processes forecast queries and maintains the configuration as new data arrives. We comprehensively evaluated our approach on real and synthetic data sets. The evaluation shows that our approach significantly increases forecast query accuracy while ensuring low model costs. <s> BIB008 </s> Forecasting with Big Data: A Review <s> Forecasting with Big Data in Population Dynamics <s> Increasingly larger data sets of processes in space and time ask for statistical models and methods that can cope with such data. We show that solutions of stochastic advectiondiusion partial dierential equations (SPDEs) provide a exible model class for spatiotemporal processes which is computationally feasible also for large data sets. The solution of the SPDE has in general a nonseparable covariance structure. Furthermore, its parameters can be physically interpreted as explicitly modeling phenomena such as transport and diusion that occur in many natural processes in diverse elds ranging from environmental sciences to ecology. In order to obtain computationally ecient statistical algorithms we use spectral methods to solve the SPDE. This has the advantage that approximation errors do not accumulate over time, and that in the spectral space the computational cost grows linearly with the dimension, the total computational costs of Bayesian or frequentist inference being dominated by the fast Fourier transform. The proposed model is applied to postprocessing of precipitation forecasts from a numerical weather prediction model for northern Switzerland. In contrast to the raw forecasts from the numerical model, the postprocessed forecasts are calibrated and quantify prediction uncertainty. Moreover, they outperform the raw forecasts. <s> BIB009
An imputation based on Neural Networks model was applied to the Norwegian population census data of 1990 in order to perform a population census by combining administrative data along with data gathered through sample surveys  . A procedure based on Neural Networks was used by BIB001 to predict trends in Spanish economic indexes per household and censal section by using the Spanish Population and Housing Census, and Family Expenditure Survey. Bayesian regression was used by BIB002 for predicting long term illness in Stockport UK by using statistics from the 1991 census. Cluster Analysis was used as a method for predicting missing data by analysing the 2007 census donor pool screening in BIB005 . Unlikely representations of farming operations in the initial Census mail list have been predicted using Classification Trees according to  BIB003 rely on a Kohonen Neural Network Clustering algorithm to find outliers and then forecast fraudulent behaviour in the data intensive Chinese telecom industry after evaluating its performance in comparison to a two-step Clustering algorithm and K-means algorithm. BIB007 uses Support Vector Machines as an auxiliary method, along with Neural Networks, and 'MapReduce' technology, for forecasting Big Data originating from China's electricity consumption. He finds the developed prediction model is able to provide sound portability and feasibility in terms of processing Big Data relating to electricity. BIB006 evaluate the use of Wavelet Transform (WT) in combination with a variety of models such as GARCH, Linear regressions, Radial Basis functions and Multilayer Perceptrons (MLP) to forecast UK gas price and electricity demand by exploiting Big Data from the British energy markets. They find that the use of WT and adaptive models can provide considerable improvements to forecasting accuracy. The conclusion here is that adaptive models combining WT with either MLP or GARCH are the optimal models for forecasting gas price and electricity demand based on the lowest mean squared error. BIB008 evaluates the use of Exponential Smoothing and ARIMA models in combination with a model configuration advisor to forecast energy demand using Big Data from an energy domain. BIB009 utilizes Stochastic Advection Diffusion Partial Differential Equations (SPDEs) to improve the precipitation forecasts for northern Switzerland using Big Data from a numerical weather prediction model. They find that following the application of SPDE, the forecasts are greatly improved in comparison to the raw forecasts attained via the numerical model. BIB004 provide theoretical evidence for the applicability of Multivariate Boosting for forecasting with Big Data. They propose a Multivariate L2 Boosting method to be used with multivariate regression and can also be applied to a VAR series. An application to 795 Arabidopsis thaliana genes is used as an example to show the appropriateness of the proposed method.