Forecasting with Big Data: A Review <s> The 'Problem' and 'Potential' of Big Data Forecasting <s> Technological advances mean that vast data sets are increasingly common. Such data sets provide us with unparallelled opportunities for modelling and predicting the likely outcome of future events. However, such data sets may also bring with them new challenges and difficulties. An awareness of these, and of the weaknesses as well as the possibilities of these large data sets, is necessary if useful forecasts are to be made. This paper looks at some of these difficulties, using illustrations with applications from various areas. <s> BIB001 </s> Forecasting with Big Data: A Review <s> The 'Problem' and 'Potential' of Big Data Forecasting <s> Understand the need for analyses of large, complex, information-rich data sets. Identify the goals and primary tasks of the data-mining process. Describe the roots of data-mining technology. Recognize the iterative character of a data-mining process and specify its basic steps. Explain the influence of data quality on a data-mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers. In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining, which produces new, nontrivial information based on the available data set. <s> BIB002 </s> Forecasting with Big Data: A Review <s> The 'Problem' and 'Potential' of Big Data Forecasting <s> There is a tremendous amount of buzz around the concept of "big data." In this article, the author discusses the origins of this trend, the relationship between big data and traditional databases and data processing platforms, and some of the new challenges that big data presents. <s> BIB003 </s> Forecasting with Big Data: A Review <s> The 'Problem' and 'Potential' of Big Data Forecasting <s> Many believe that "big data" will transform business, government and other aspects of the economy. In this article we discuss how new data may impact economic policy and economic research. Large-scale administrative datasets and proprietary private sector data can greatly improve the way we measure, track and describe economic activity. They also can enable novel research designs that allow researchers to trace the consequences of different events or policies. We outline some of the challenges in accessing and making use of these data. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in economics. <s> BIB004 </s> Forecasting with Big Data: A Review <s> The 'Problem' and 'Potential' of Big Data Forecasting <s> Abstract In this paper, I review the recent emergence of three groups of data sources and assess some of the opportunities and challenges they pose for the understanding of cities, particularly in the context of the Regional Science and urban research agenda. These are data collected from mobile sensors carried by individuals, data derived from businesses moving their activity online and government data released in an open format. Although very different from each other, they are all becoming available as a side-effect since they were created with different purposes but their degree of popularity, pervasiveness and ease of access is turning them into interesting alternatives for researchers. Existing projects and initiatives that conform to each class are featured as illustrative examples of these new potential sources of knowledge. <s> BIB005
There exists a widespread belief that Big Data can aid in improving forecasts provided that we can analyse and discover hidden patterns, and  agree that predictions can be improved through data driven decision making.  believes Big Data will soon be predicting our every move, and according to BIB004 , Big Data is most commonly sought after for building predictive models in a world where forecasting continues to remain a vital statistical problem BIB001 ). We then come to the question, what is the problem behind forecasting with Big Data? The simplest explanation is that the traditional forecasting tools cannot handle the size, speed and complexity inherent in Big Data BIB003 . According to BIB005 this is owing to the lack of a structure in these data sets and the size. As a result, traditional techniques are seldom preferred for tackling Big Data BIB005 . Therefore, forecasting Big Data poses a challenge to organizations, and this is further highlighted by the European Central Bank which is conducting an entire workshop dedicated to using Big Data for forecasting 1 .  is first to discuss the importance of data volume, velocity and variety in the context of Big Data. A decade later,  ,  , and Shi (2014) identifies these '3V's' as three concepts which define the dimensions of Big Data.  believe Data Mining techniques can be exploited to help forecasting with Big Data, a view supported by  . However, it should be noted that in the past, Data Mining techniques have mainly been used on static data as opposed to time series (see for example  ; Pyle (2003); Krugan and Musilek (2006) ; BIB002 ). Interestingly, Cukier (2010) finds fault with Big Data for the recent financial crisis as he believes the financial models adopted were unable to handle the huge amounts of data that was being inputted into the systems, and thereby resulted in inaccurate forecasts. The opportunities for gains through forecasting with Big Data are diverse. At present, there is increased research into using Big Data for obtaining accurate weather forecasts and the initial results suggests that Big Data will benefit weather forecasts immensely   . In fact, weather forecasting has been one of the main beneficiaries of Big Data, but the forecasts are still inaccurate beyond a week (Silver 2013) . The fashion industry too is exploiting Big Data forecasts with companies such as EDITD (http://editd.com/) using Big Data for forecasting the fashion future by collecting data from social media  . According to  , the airline industry is yet another field where Big Data forecasting is crucial. An interesting success story behind forecasting with Big Data is Netflix and its use of Big Data forecasts for decision making prior to commencing production of its own TV show 'House of Cards', and this resulted in increased revenue for the company (KRWG, 2013) . The potential underlying Big Data forecasts is truly astonishing and at times 'scary' as was evident in the experience of an individual in a story narrated by Duhigg (2012) where an irate customer walks into a 'Target' store in Minneapolis to complain about the store sending coupons relating to pregnancy products to his high school daughter. A few weeks later the same customer apologizes to the manager as following a discussion with his daughter it was revealed that she was in fact pregnant (Duhigg, 2012) .