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Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system. Consequently, it fosters reasonable scheduling plans, ensuring the safety of the system and improving the economic dispatching efficiency of the power system.

First, a new seasonal grey buffer operator in the longitudinal and transverse dimensional perspectives is designed. Then, a new seasonal grey modeling approach that integrates the new operator, full real domain fractional order accumulation generation technique, grey prediction modeling tool and fruit fly optimization algorithm is proposed. Moreover, the rationality, scientificity and superiority of the new approach are verified by designing 24 seasonal electricity consumption forecasting approaches, incorporating case study and amalgamating qualitative and quantitative research.

Compared with other comparative models, the new approach has superior mean absolute percentage error and mean absolute error. Furthermore, the research results show that the new method provides a scientific and effective mathematical method for solving the seasonal trend power consumption forecasting modeling with impact disturbance.

Considering the development trend of longitudinal and transverse dimensions of seasonal data with impact disturbance and the differences in each stage, a new grey buffer operator is constructed, and a new seasonal grey modeling approach with multi-method fusion is proposed to solve the seasonal power consumption forecasting problem.

The highlights of the paper are as follows:

1. A new seasonal grey buffer operator is constructed.

2. The impact of shock perturbations on seasonal data trends is effectively mitigated.

3. A novel seasonal grey forecasting approach with multi-method fusion is proposed.

4. Seasonal electricity consumption is successfully predicted by the novel approach.

5. The way to adjust China's power system flexibility in the future is analyzed.

A new seasonal grey buffer operator is constructed.

The impact of shock perturbations on seasonal data trends is effectively mitigated.

A novel seasonal grey forecasting approach with multi-method fusion is proposed.

Seasonal electricity consumption is successfully predicted by the novel approach.

The way to adjust China's power system flexibility in the future is analyzed.

The need effectively to integrate decision making tasks together with knowledge representation and inference procedures has caused recent research efforts towards the integration of decision support systems with knowledge‐based techniques. Explores the potential benefits of such integration in the area of business forecasting. Describes the forecasting process and identifies its main functional elements. Some of these elements provide the requirements for an intelligent forecasting support system. Describes the architecture and the implementation of such a system, the theta intelligent forecasting information system (TIFIS) that that first‐named author had developed during his dissertation. In TIFIS, besides the traditional components of a decision‐support onformation system, four constituents are included that try to model the expertise required. The information system adopts an object‐oriented approach to forecasting and exploits the forecasting engine of the theta model integrated with automated rule based adjustments and judgmental adjustments. Tests the forecasting accuracy of the information system on the M3‐competition monthly data.

This paper aims to examine the stability of the currency demand function for India with private consumption expenditure, tax–gross domestic product ratio and deposit rate as explanatory variables for the period 1996:1 to 2014:4. Additionally, this paper also tries to detect the presence of endogenous financial innovation in the currency demand function.

For the theoretical foundation of the study, this paper has used a modified version of money-in-the-utility function. To examine the stability of currency demand function empirically, seasonal cointegration technique developed by HEGY (1990) and EGHL (1993) was applied. Finally, to detect the presence of endogenous financial innovation in the currency demand equation, the Gurley and Shaw (1960) hypothesis was tested by presenting the currency demand equation in a state–space form.

The empirical findings show that there is the absence of long-run cointegrationg relationship among the variables at the zero and annual frequency; however, there is evidence of a relationship among the variables at the biannual frequency. Moreover, the time-varying coefficient of deposit rate elasticity, used to test the Gurley–Shaw hypothesis, suggests that innovations in financial markets, especially improvements in the payment technology, raise the deposit-rate elasticity, beginning from 2010 onward.

The empirical results of the paper suggest that there would be shrinkage of currency demand in future. From the monetary policy angle, the Reserve Bank of India needs to adapt adequately to a situation of shrinking demand for currency.

Apart from using seasonally unadjusted data to examine currency demand function for India, this study, for the first time, and to the best of the authors’ knowledge, tries to test the evidence of financial innovation in India by testing the Gurley–Shaw hypothesis. The findings of the study will have significant implication in the planning of the issue and distribution of currency in the fast-changing economic environment.

Soon after beginning operations, the Federal Reserve established a nationwide network for collecting information about the economy. In 1919, the Fed began tabulating data by about retail sales, which it viewed as a fundamental measure of consumption. From 1920 until 1929, the Federal Reserve published data about retail sales each month by Federal Reserve district, but ceased to do so after 1929. It continued to compile monthly data on retail sales by reserve district, but this data remained in house. We collected these in-house reports from the archives of the Board of Governors and constructed a consistent series on retail trade at the district level. The new series enhances our understanding of economic trends during the Roaring ‘20s and Great Depression.

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

Forecasting is a vital part of hospitality operations because it allows businesses to make imperative decisions, such as pricing, promotions, distribution, scheduling, and arranging facilities, based on the predicted demand and supply. This chapter covers three main concepts related to forecasting: it provides an understanding of hospitality demand and supply, it introduces several forecasting methods for practical application, and it explains yield management as a function of forecasting. In the first section, characteristics of hospitality demand and supply are described and several techniques for managing demand and supply are addressed. In the second section, several forecasting methods for practical application are explored. In the third section, yield management is covered. Additionally, examples of yield management applications from airlines, hotels, and restaurants are presented.

The purpose of this paper is to examine whether China is still a passive price taker from the US soybean futures, or instead domestic futures market has developed certain degrees of pricing power through time. The finding helps to identify the importance of China soybean futures in the perspective of portfolio selection for international futures traders. If China soybean futures market is no longer a price taker after the subprime crisis, traders need to include it as a separate category in their portfolio.

This paper uses exponential generalized autoregressive conditional heteroskedasticity-generalized error distribution (EGARCH-GED) and generalized autoregressive conditional heteroskedasticity-generalized error distribution (GARCH-GED) models to test spillover effects between Dalian Commodity Exchange (DCE) and Chicago Board of Trade (CBOT) soybean futures. The authors divide daily samples into three subperiods based on the subprime crisis. Three research questions – whether China is still the price taker, the importance of Chinese soybean futures in international futures portfolio selection, and the influences of subprime crisis on soybean futures volatility relationship – are examined by comparing estimation results through time and different contracts.

The spillover effect from CBOT soybean futures to DCE No. 1 soybean futures becomes weaker through time. China is no longer a soybean futures price taker after the subprime crisis. The authors also find the shocks of bearish news on DCE soybeans are greater than those of bullish news. Potential volatility of DCE in long positions is bigger than that in short positions.

China is the largest soybean importer. DCE is a very important futures market for non-genetically modified soybeans. It is necessary for both international and domestic futures traders to understand the changes in international soybean futures price relationship and take corresponding strategies. It is also important for market to realize that DCE soybean futures are to a less degree price taker after the subprime crisis.

The paper applies EGARCH-GED and GARCH-GED models to identify changes in spillover effects before, during, and after the subprime crisis. Different from other studies, this paper finds after the subprime crisis, China is no longer the soybean futures price taker. This paper also compares the spillover effects of non-genetically modified soybean futures (No. 1 soybean futures) with genetically modified soybean futures (No. 2 soybean futures).

This paper aims to present a chronology of Indian business cycles in the post-reform period. In India, earlier, macroeconomic shocks were about droughts and oil prices. Economic reforms have led to an interplay of a market economy, financial globalisation and decisions of private firms to undertake investment and hold inventory. This has changed the working of the business cycle and has raised concerns about business-cycle stabilisation. In the backdrop of these developments, the macroeconomics research agenda requires foundations of measurement about business-cycle phenomena. One element of this is the identification of dates of business-cycle turning points.

This paper uses the growth-cycle approach to present the chronology of business cycles. The paper uses the Christiano–Fitzgerald (CF) filter to extract the cyclical component and shows the robustness of the findings to the contemporary methods of cycle extraction. It then applies the Bry–Boschan algorithm to identify the dates of peaks and troughs.

The paper finds three periods of recession. The first recession was from 1999-Q4 to 2003-Q1; the second recession was from 2007-Q2 to 2009-Q3; and the third recession ran from 2011-Q2 till 2012-Q4. These results are robust to the choice of filter and to the choice of the business-cycle indicator. These dates suggest that, on average, expansions in India are 12 quarters in length and recessions run for 9 quarters. The paper offers evidence of change in the nature of cycles.

Dates of business-cycle turning points are a critical input for academic and policy work in macroeconomics. The paper offers robust estimation of the business-cycle turning points in the post-reform period using contemporary techniques of cycle extraction. This work helps lay the foundations for downstream macroeconomics research by academicians and policymakers.

Little research on the influence of external factors, such as weather and holiday periods, on retail sales on alcoholic beverages is available. This study aims to investigate how weekly retail sales of different alcoholic beverages vary in association with daily maximum temperatures and annual federal holidays across selected US counties in the years 2013 to 2015. The research provides information, which can contribute to better sales forecasts.

Secondary data of weekly retail sales (volume) of alcoholic beverages from 37,346 stores in 651 counties in the USA are analysed. The data cover on average 21% of all existing US counties and 12% of the total US off-trade retail sales of alcoholic beverages in the period studied (Euromonitor, 2017). Additional data of federal holidays and meteorological data are collated for each county in the sample. Seasonal autoregressive integrated moving average models with exogenous regressors (SARIMAX) are applied to develop forecasting models and to investigate possible relationships and effects.

The results indicate that off-trade retail sales of beer, liquor, red and white wine are temperature sensitive throughout the year, while contrary to expectations rosé, sparkling and other wines are not. Sales sensitivities to temperature also differ by geography. In the warmest regions, liquor and white wine sales do not respond to temperature changes, as opposed to the coolest regions, where they are responsive. Public holidays, particularly Easter, Thanksgiving, Christmas and New Year holidays, represent a constant influencing factor on short-term sales increases for all investigated alcoholic beverage categories.

This is the first large-scale study of weather and holiday-related sales variations over time, across geographies and different alcoholic beverage categories. Seasonal and non-seasonal short-term sales variations are important for retailers and manufacturers alike. Accounting for expected changes in demand accommodates efficiencies along the supply chain and has implications for retail management, as well as adjusting marketing efforts in competing categories.