Comparative study on retail sales forecasting between single and combination methods

In today’s competitive global economy, businesses must adjust themselves constantly to ever-changing markets. Therefore, predicting future events in the marketplace is crucial to the maintenance of successful business activities. In this study, sales forecasts for a global furniture retailer operating in Turkey were made using state space models, ARIMA and ARFIMA models, neural networks, and Adaptive Network-based Fuzzy Inference System (ANFIS). Also, the forecasting performances of some widely used combining methods were evaluated by comparison with the weekly sales data for ten products. According to the best of our knowledge, this study is the first time that the recently developed state space models, also called ETS (Error-Trend-Seasonal) models, and the ANFIS model have been tested within combining methods for forecasting retail sales. Analysis of the results of the single models in isolation indicated that none of them outperformed all the others across all the time series investigated. However, the empirical results suggested that most of the combined forecasts examined could achieve statistically significant increases in forecasting accuracy compared with individual models and with the forecasts generated by the company’s current system

Zonal And Regional Load Forecasting In The New England Wholesale Electricity Market: A Semiparametric Regression Approach

Power system planning, reliability analysis and economically efficient capacity scheduling all rely heavily on electricity demand forecasting models. In the context of a deregulated wholesale electricity market, using scheduling a region’s bulk electricity generation is inherently linked to future values of demand. Predictive models are used by municipalities and suppliers to bid into the day-ahead market and by utilities in order to arrange contractual interchanges among neighboring utilities. These numerical predictions are therefore pervasive in the energy industry. This research seeks to develop a regression-based forecasting model. Specifically, electricity demand is modeled as a function of calendar effects, lagged demand effects, weather effects, and a stochastic disturbance. Variables such as temperature, wind speed, cloud cover and humidity are known to be among the strongest predictors of electricity demand and as such are used as model inputs. It is well known, however, that the relationship between demand and weather can be highly nonlinear. Rather than assuming a linear functional form, the structural change in these relationships is explored. Those variables that indicate a nonlinear relationship with demand are accommodated with penalized splines in a semiparametric regression framework. The equivalence between penalized splines and the special case of a mixed model formulation allows for model estimation with currently available statistical packages such as R, STATA and SAS. Historical data are available for the entire New England region as well as for the smaller zones that collectively make up the regional grid. As such, a secondary research objective of this thesis is to explore whether or not an aggregation of zonal forecasts might perform better than those produced from a single regional model. Prior to this research, neither the applicability of a semiparametric regression-based approach towards load forecasting nor the potential improvement in forecasting performance resulting from zonal load forecasting has been investigated for the New England wholesale electricity market

The beautiful game? an econometric study of audiences, gambling and efficiency in English football

Football is the world's most watched sport. This thesis investigates three related aspects of football in England using econometric techniques. An investigation of the reasons why people watch football, both live and televised matches is undertaken. Particular attention is paid to outcome uncertainty, both match and seasonal. Two equations are developed to explain match attendance and BSkyB television audiences for the 1993/94 English Premier League season. In the match attendance equation capacity constraints are accounted for by use of the Tobit model. It is found that quality factors, outcome uncertainty and supporter loyalty are all important determinants of football attendances but that televising a match on BSkyB does not significantly affect audiences. The second study focuses on the efficiency of the fixed odds betting market for football in England. It is the efficiency of how market participants utilise available information that is tested. A model of bookmaker behaviour is presented in which the bookmaker maximises their expected share of the total amount bet. It is found that an expected profit maximising bookmaker could set market inefficient odds. Several empirical tests using the ordered probit model with data on prices, publicly available information and experts' predictions are carried out. Evidence of market inefficiency is identified offering profitable betting opportunities. Productive efficiency of football clubs is the focus of the third study. It investigates how efficiently clubs utilise their inputs to produce playing success. Unlike most previous sports productive efficiency studies, true inputs (i.e. playing ability proxied by wages) and not intermediate outputs (e.g. goals scored) are used in the efficiency estimations. Two techniques, econometrics and Data Envelopment Analysis are used, allowing a useful comparison of their relative benefits. Efficient clubs are identified and the features which make them efficient discussed

Does the inclusion of climate variables improve tourism demand forecasting performance?

The aim of this study is to assess whether incorporating climate variables in econometric and combination forecasting models can improve tourism demand forecasting performance. Climate conditions are important tourism resources which can influence tourists’ decision as to when and where to travel, however, our understanding of the value of climate variables in forecasting tourism demand is limited. The current research fills this gap through empirical studies on UK’s international tourism demand. Inbound tourism demand to the UK from seven leading markets, namely, France, Germany, Irish Republic, Italy, the Netherlands, Spain and the US are studied respectively based on quarterly time series data from 1994Q1 to 2017Q4. The bounds test cointegration approach is applied to assess the long-run relationships between tourism demand and its influencing factors and to evaluate the impact of climate on tourism demand. Individual tourism demand forecasts are generated through both causal econometric and non- causal time series models, which are popular in the current tourism demand literature. Causal econometric models, which consist of the bounds test cointegration approach, the autoregressive distributed lag model (ADLM), the leading indicator (LI) model, the vector autoregressive (VAR) model, the time-varying parameter (TVP) model and the simple dynamic (SD) model, take two model specifications, which are different in identified influencing factors. Econometric models that only consider economic factors as demand determinants are named as traditional econometric models, and the others that include the climate factor as a demand determinant are called climate econometric models. Non-causal time series techniques consist of the seasonal naïve no-change model, the seasonal autoregressive integrated moving average (SARIMA) model, the exponential smoothing (ETS) model and the state space ETS model. One- to four-step-ahead out-of-sample single forecasts are generated from every individual forecasting model through the recursive forecasting procedure with the seasonal naïve no-change model serving as the benchmark. Except the naïve model, all other individual forecasting models are selected as candidate constituents for combination. For combination forecasting, the 15 selected individual models are categorized into three groups. The first group includes all individual models; the second one contains traditional econometric and time series models; and the third category consists of climate econometric and time series models. Combination is conducted for each group respectively, resulting in three sets of combination forecasts: the first set is generated through combining 15 individual models; the second and third ones are produced from integrating 9 individual models. Different combination methods are applied including the simple average (SA) method, the variance-covariance (VACO) method, the discounted mean square forecast error (DMSFE) (α = 0.85/0.90/0.95 ) methods, as well as the newly-introduced inverse-MAE and the two-stage combination approaches. Comprehensive comparisons of the predictive powers of the individual and combination forecasting approaches for seven origins and four forecasting horizons are conducted based on three accuracy measures including mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). The results show that individual model’s forecasting performance varies greatly according to the origin market under consideration. No single model can perform the best in all cases, and in most cases, more advanced individual models forecast better than the naïve benchmark. In general, non-causal time series techniques are superior to causal econometric models. Whether including the climate factor can improve the forecasting accuracy of econometric models should be evaluated case-by-case. With respect to the forecasting ability of the combination approach, it is demonstrated that combining individual forecasts is beneficial regardless of origin country under study, forecasting horizon under consideration, accuracy measure used, combination methods applied or combination group under analysis. In all cases, there are always a portion of combination forecasts that are more accurate than the best single projections, and the worst forecasts are always produced by individual forecasting models. It means that the combination forecasting approach is superior to the individual one, as it can improve forecasting accuracy and reduce forecasting failure. Comparisons among alternative combination methods show that no single combination method can provide the best composite forecasts in all situations. The newly-introduced inverse-MAE scheme performs quite well, but the two-stage combination methods behave unsatisfactorily. Comparisons among three combination groups reveal that, generally, combining all individual models, which include traditional econometric models, climate econometric models and time series techniques produce the best combination forecasts, which means that combining econometric models with different influencing factors and introducing climate variables into combination can contribute to more accurate projections. It implies that through combining, diversity gain can be achieved not only by incorporating different modelling techniques but also by integrating different model specifications. Regarding which and how many models to combine, it is shown that individual models’ frequencies to constitute the superior combination forecasts are irrelevant to their forecasting abilities. More accurate individual forecasts do not have higher opportunities to construct superior composite projections. The number of single constituents in the best forecasts range from two to six, and for most origins, combining two individual models can bring about the most accurate projections. To the best of my knowledge, this research represents the first effort to evaluate the combination forecasting approach which consider econometric models with different explanatory variables as candidate constituents, and climate variables have been, for the first time, introduced to the combination forecasts. It proves that better combination forecasts can be obtained by integrating econometric models with different influencing factors, and the value of non-economic explanatory variables in combination forecasting deserves more attention. It is suggested that a user-friendly software for combination forecasting should be made available and combination forecasts should be included in forecasting comparisons considering the general superiority of the combination forecasting approach compared to the single forecasting method

Seasonal forecasting of reservoir inflows in data sparse regions

Management of large, transboundary river systems can be politically and strategically problematic. Accurate flow forecasting based on public domain data offers the potential for improved resource allocation and infrastructure management. This study investigates the scope for reservoir inflow forecasting in data sparse regions using public domain information. Four strategically important headwater reservoirs in Central Asia are used to pilot forecasting methodologies (Toktogul, Andijan and Kayrakkum in Kyrgyzstan and Nurek in Tajikistan). Two approaches are developed. First, statistical forecasting of monthly inflow is undertaken using relationships with satellite precipitation estimates as well as reanalysis precipitation and temperature products. Second, mean summer inflows to reservoirs are conditioned on the tercile of preceding winter large scale climate modes (El Niño Southern Oscillation, North Atlantic Oscillation, or Indian Ocean Dipole). The transferability of both approaches is evaluated through implementation to a basin in Morocco. A methodology for operationalising seasonal forecasts of inflows to Nurek reservoir in Tajikistan is also presented. The statistical models outperformed the long-term average mean monthly inflows into Toktogul and Andijan reservoirs at lead times of 1-4 months using operationally available predictors. Stratifying models to forecast monthly inflows for only summer months (April-September) improved skill over long term average mean monthly inflows. Individual months Niño 3.4 during October-January were significantly (p < 0.01) correlated to following mean summer inflows Toktogul, Andijan and Nurek reservoirs during the period 1941-1980. Significant differences (p < 0.01) occurred in summer inflows into all reservoirs following opposing phases of winter Niño 3.4 during the period 1941-1980. Over the period 1941-2016 (1993-1999 missing), there exists only a 22% chance of positive summer inflow anomalies into Nurek reservoir following November-December La Niña conditions. Cross validated model skill assessed using the Heidke Hit Proportion outperforms chance, with a hit rate of 51-59% depending upon the period of record used. This climate mode forecasting approach could be extended to natural hazards (e.g. avalanches and mudflows) or to facilitate regional electricity hedging (between neighbouring countries experiencing reduced/increased demand). Further research is needed to evaluate the potential for forecasting winter energy demand, potentially reducing the impact of winter energy crises across the region

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

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