Forecasting with Big Data: A Review <s> Signal and Noise <s> In this paper, the performance of the Singular Spectrum Analysis (SSA) technique is assessed by applying it to 24 series measuring the monthly seasonally unadjusted industrial production for important sectors of the German, French and UK economies. The results are compared with those obtained using the Holt-Winters' and ARIMA models. All three methods perform similarly in short-term forecasting and in predicting the direction of change (DC). However, at longer horizons, SSA significantly outperforms the ARIMA and Holt-Winters' methods. <s> BIB001 </s> Forecasting with Big Data: A Review <s> Signal and Noise <s> The incessantly growing demand for energy consumption and the significance of the availability of sustainable energy for achieving long term economic growth defines the importance of forecasting energy statistics. This paper analyses and forecasts actual energy consumption data for EU-27 nations using both parametric and nonparametric time series forecasting techniques. Singular Spectrum Analysis (SSA) is adopted as the nonparametric time series analysis and forecasting technique and the results from SSA are compared with ARIMA, which is a parametric forecasting technique. <s> BIB002 </s> Forecasting with Big Data: A Review <s> Signal and Noise <s> This paper seeks to provide an alternative forecast to that provided by the Energy Information Administration (EIA) on energy-related monthly CO2 emissions in the United States. The data on CO2 emissions from petroleum, natural gas, coal and total fossil fuels obtained via the EIA covering the period January 2005 to November 2013 is analysed and then forecasted using ARIMA, Holt-Winters, and Exponential Smoothing prior to introducing the Singular Spectrum Analysis (SSA) technique for CO2 emissions forecasting. A new combination forecast (EIA-SSA) is also introduced by merging the SSA and EIA forecasts, and is seen outperforming all models including the EIA forecast. Finally, the EIA-SSA model is used to provide an alternative 12 month ahead outlook for US energy-related CO2 emissions from December 2013 to November 2014. This research is expected to influence the methodology adopted by the EIA for forecasting CO2 emissions in the future by improving the accuracy of the forecasts, and the impact of this study will be clearer upon comparing the actual CO2 emissions in US with the EIA, and EIA-SSA forecasts over the 12 month period which follows. <s> BIB003 </s> Forecasting with Big Data: A Review <s> Signal and Noise <s> This paper is aimed at introducing the powerful, nonparametric time series analysis and forecasting technique of Singular Spectrum Analysis (SSA) for trade forecasting via an application which evaluates the impact of the 2008 recession on U.S. trade forecasting models. This research is felicitous given the magnitude of the structural break visible in the U.S. trade series following the 2008 economic crisis. Structural breaks resulting from such recessions might affect conclusions from traditional unit root tests and forecasting models which make use of these tests. As such, it is prudent to evaluate the sensitivity and reliability of parametric, historical trade forecasting models in comparison to the relatively modern, nonparametric models. In doing so, we introduce the SSA technique for trade forecasting and perform exhaustive statistical tests on the data for normality, stationarity and change points, and the forecasting results for statistical significance prior to reaching the well-founded conclusion that SSA is less sensitive to the impact of recessions on U.S. trade, in comparison to an optimised ARIMA model, Exponential Smoothing and Neural Network models. Ergo, we conclude that SSA is able to provide more accurate forecasts for U.S. trade in the face of recessions, and is therefore presented as an apt alternative for U.S. trade forecasting before, during and after a future recession. <s> BIB004
A more technical, but extremely important challenge in Big Data forecasting is identified by Silver (2013) . He suggests that noise is distorting the signal in Big Data, and that there is an increasing noise to signal ratio visible in Big Data. Silver's (2013) notion is further confirmed via Ba bura and Modugno (2014) who points out that with large data sets, extracting the signal is made more complex. A majority of traditional forecasting techniques forecast both the noise and signal, and whilst they perform relatively well in the case of traditional data sets, the increasing noise to signal ratio seen in Big Data is more likely to distort the accuracy of forecasts. This suggests that there is a need for employing and evaluating the use of forecasting techniques which can filter the noise in Big Data and forecast the signal alone. A sound example of a filtering technique is Singular Spectrum Analysis (SSA) which seeks to filter the noise from a given time series, reconstruct a new series which is less noisy, and then use this newly reconstructed series for forecasting future data points. The superiority of the methodology of SSA over traditional techniques has been proven recently in a variety of fields where the data sets have comparatively smaller signal to noise ratios in relation to the considerably higher signal to noise ratios expected in Big Data (see for example, BIB001 , BIB002 , BIB003 and BIB004 ). Future research should concentrate on evaluating the applicability of such techniques for filtering the noise in Big Data to enable accurate and meaningful forecasts.