Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis

Hassani, Hossein; Heravi, Saeed; Zhigljavsky, Anatoly

doi:10.1002/for.2244

Cited by 92 publications

(75 citation statements)

References 60 publications

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“…The use of MDM is common practice in forecasting because it is found to be robust in assessing the significance of observed differences between the performances of two forecasts (see Barhoumi et al (2012) and Hassani et al (2012)). MDM also overcomes the problem of over-sized DMs in moderate samples.…”

Section: The Worst Performances Are Observed In Thementioning

confidence: 99%

Inflation and Unemployment Forecasting with Genetic Support Vector Regression

Sermpinis

Stasinakis

Theofilatos

et al. 2014

Journal of Forecasting

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In this paper a hybrid Genetic Algorithm -Support Vector Regression (GA-SVR) model in economic forecasting and macroeconomic variable selection is introduced. The proposed algorithm is applied to the task of forecasting the US inflation and unemployment. The GA-SVR genetically optimizes the SVR parameters and adapts to the optimal feature subset from a feature space of potential inputs. The feature space includes a wide pool of macroeconomic variables that might affect the two series under study.

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Section: The Worst Performances Are Observed In Thementioning

confidence: 99%

Inflation and Unemployment Forecasting with Genetic Support Vector Regression

Sermpinis

Stasinakis

Theofilatos

et al. 2014

Journal of Forecasting

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“…The DC criterion is a measure of the percentage of forecasts that accurately predict the direction 482 of change (Hassani, Heravi, & Zhigljavsky, 2012 …”

mentioning

confidence: 99%

Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis

et al. 2015

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5This paper introduces Singular Spectrum Analysis (SSA) for tourism demand forecasting 6 via an application into total monthly U.S. Tourist arrivals from 1996-2012. The global 7 tourism industry is today, a key driver of foreign exchange inflows to an economy. Here, we 8 compare the forecasting results from SSA with those from ARIMA, Exponential Smoothing 9 (ETS) and Neural Networks (NN). We find statistically significant evidence proving that 10 the SSA model outperforms the optimal ARIMA, ETS and NN models at forecasting total 11 U.S. Tourist arrivals. The study also finds SSA outperforming ARIMA at forecasting U.S. 12Tourist arrivals by country of origin with statistically significant results. In the process, we 13 find strong evidence to justify the discontinuation of employing ARIMA, ETS and a feed-14 forward NN model with one hidden layer as a forecasting technique for U.S. Tourist arrivals 15 in the future, and introduce SSA as its highly lucrative replacement. rather than weakened this need to forecast tourist demand accurately. 25As discussed in the following section there is an extensive and high profile existing literature 26 on forecasting tourism demand. This literature covers a wide range of different forecasting 27 techniques, applied to a wide range of different countries or locations. The purpose of this paper 28 is to add to this literature by introducing a new model for forecasting tourist arrivals and to 29 apply it to inbound U.S. Tourist arrivals. Forecasting U.S. Tourist arrivals is both a demanding 30 and important task, mainly because these data exhibit a high degree of fluctuation over time. 31Figure 1 depicts the time series for total monthly U.S. Tourist arrivals between January 1996 and 32 November 2012. A first look at the time series suggests signs of seasonality in U.S tourist arrivals. 33The figure also shows that the tourism industry in the U.S. is experiencing rapid development in There are a number of components which define a good demand forecasting model for tourism Australia, concluding that using models expressed in first differences increased forecast accuracy. Using data for Hong Kong they find use of tourism arrivals to be more affected by income in 117 the country of origin and tourism expenditure to be more sensitive to prices. Wan, Wang, and 118Woo (2013), also using tourist arrival data for Hong Kong, assess the properties of disaggregated 119 forecasts using a seasonal ARIMA model relative to aggregate forecasts. They find the sum of 120 disaggregated forecasts to provide greater accuracy than an aggregate forecast. 121A very closely related strand in the literature seeks to combine two or more forecasting 122 models into a new hybrid model and to test whether this results in greater forecast accuracy. 123Andrawis, Atiya, and El-Shishiny (2011) finds that, in forecasts of tourism arrivals into Egypt, 124combining short and long term forecasts improves accuracy compared to the individual forecasts. 125Cang (2011)

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“…A comparison to other models was presented, and the results showed that SSA is superior to other benchmarking models. Also, Hassani et al [17] developed the multivariate SSA (MSSA) technique and demonstrated that MSSA can be a powerful method for time series analysis and forecasting. UK Industrial Production series were used to illustrate the main findings, and the result showed better accuracy compared with the autoregressive integrated moving average (ARIMA) and vector autoregressive (VAR) models.…”

Section: On Developments and Applications Of Singular Spectrum Analysismentioning

confidence: 99%

Modeling the Trend of Credit Card Usage Behavior for Different Age Groups Based on Singular Spectrum Analysis

Nai¹,

Liu

Wang³

et al. 2018

Algorithms

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Credit card holders from different age groups have different usage behaviors, so deeply investigating the credit card usage condition and properly modeling the usage trend of all customers in different age groups from time series data is meaningful for financial institutions as well as banks. Until now, related research in trend analysis of credit card usage has mostly been focused on specific group of people, such as the behavioral tendencies of the elderly or college students, or certain behaviors, such as the increasing number of cards owned and the rise in personal card debt or bankruptcy, in which the only analysis methods employed are simply enumerating or classifying raw data; thus, there is a lack of support in specific mathematical models based on usage behavioral time series data. Considering that few systematic modeling methods have been introduced, in this paper, a novel usage trend analysis method for credit card holders in different age groups based on singular spectrum analysis (SSA) has been proposed, using the time series data from the Survey of Consumer Payment Choice (SCPC). The decomposition and reconstruction process in the method is proposed. The results show that the credit card usage frequency falls down from the age of 26 to the lowest point at around the age of 58 and then begins to increase again. At last, future work is discussed.

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Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis

Cited by 92 publications

References 60 publications

Inflation and Unemployment Forecasting with Genetic Support Vector Regression

Inflation and Unemployment Forecasting with Genetic Support Vector Regression

Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis

Modeling the Trend of Credit Card Usage Behavior for Different Age Groups Based on Singular Spectrum Analysis

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