Forecasting Procedures Research Articles

Demand forecasting in pharmaceutical supply chains: A case study

Demand forecasting plays a critical role in logistics and supply chain management. In the paper, state-of-art methods and key challenges in demand forecasting for the pharmaceutical industry are discussed. An integrated procedure for in-market product demand forecasting and purchase order generation in the pharmaceutical supply chain is described. A case study for supply of pharmaceutical products from a wholesaler to a distribution company located in an emerging market is presented. Alternative forecasting scenarios for thebaseline demand calculations using the SMA model, multiple linear regressions and symbolic regression with genetic programming are experimentally investigated, and their practical implicationsare discussed.

Tropical Cyclone Forecast Verification by India Meteorological Department for North Indian Ocean: a Review

ABSTRACT The tropical cyclone (TC) forecast verification procedure followed by India Meteorological Department (IMD) is reviewed herewith as compared to the standard prescribed by World Meteorological Organisation. The limitations in the present procedure and the future scope are presented and analysed. The IMD has considerably increased its efforts in recent years in the areas of TC forecasting and verification. Many facilities have been developed to enable a detailed assessment of the global model’s performance in the forecasting of TCs as well as operational TC forecast performance. Further development of these facilities is being undertaken by IMD. There are grey areas in TC forecast verification of IMD in the field of calculation of forecast error in surface wind radii in geographical quadrants, which is yet to start. Also there is need for improvement in verification of storm surge, wave height, coastal inundation forecasts and heavy rainfall forecasts by calculating the skill parameters and also by improving the analysis data required for verification of forecasts. Similarly there is need for verification of strike probability as well as dynamical cone of uncertainty as given by ensemble prediction system.

ARIMA model forecast based on EViews software

Time series is a series of data obtained in chronological order. Future values of most time series can be forecasted according to current values and past values. The EViews software is a software package specifically designed to process time series data. Autoregressive Integrated Moving Average (ARIMA) model, a time series forecast method, can be achieved with the EViews software. Based on the EViews software, the forecast procedure with ARIMA model is illustrated in this work. As an example, the Gross Domestic Product (GDP) of China is forecasted from 2016 to 2018.

Quantile smoothing in supply chain and logistics forecasting

In this paper, we re-examine the exponential smoothing model in the context of its use in supply chain and logistics forecasting when supply chain or logistics costs are measured by the doubly linear (LINLIN) loss function of errors of demand forecasts. The parameters of our models are optimized with respect to the LINLIN loss, i.e., by applying the quantile regression estimator. The resulting forecasting procedures, which we call quantile smoothing, are then used to forecast the monthly microeconomic time series and the quarterly and annual sales data from the M3 forecast competition. According to accuracy measures for quantile predictions and in part also statistical tests of the forecasting results, the suggested procedures lead to better quantile forecasts than both standard econometric approaches and popular methods from the practice of logistics forecasting in the case of the longer among the microeconomic monthly data. On the contrary, for the other time series, the best outcomes are usually obtained through a simple modeling of the conditional median or mean, although quantile smoothing still remains among the best forecasting procedures. Moreover, the best performing quantile smoothing equation changes across quantiles.

Judgmental adjustments in tourism forecasting practice: How good are they?

This study aims to evaluate the accuracy of different judgmental forecasting tasks, compare the judgmental forecasting behaviour of tourism researchers and practitioners and explore the validity of experts’ judgmental behaviour by using the Hong Kong visitor arrivals forecasts over the period 2011Q2−2015Q4. Delphi-based judgmental forecasting procedure was employed through the Hong Kong Tourism Demand Forecasting System, an online forecasting support system, to collect and combine experts’ adjusted forecasts. This study evaluates forecasting performance and explores the characteristics of judgmental adjustment behaviour through the use of a group of error measures and statistical tests. The findings suggest a positive correlation between forecast accuracy and the level of data variability, and that experts’ adjustments are more beneficial in terms of achieving higher accuracy for series with higher variability. Industry practitioners’ forecasts outperformed academic researchers, particularly in making short-term forecasts. However, no significant difference was found between the two panels in making directionally correct forecasts. Experts’ judgmental intervention was found most useful for those series most in need of adjustment. The size of adjustment was found to have a strong and significantly positive association with the direction of forecast adjustment, but no statistically significant evidence was found regarding the relationship between accuracy improvement and adjustment size.

Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting

The inclusion of photo-voltaic generation in the distribution grid poses technical difficulties related to the variability of the solar source and determines the need for Probabilistic Forecasting procedures (PF). This work describes a new approach for PF based on quantile regression using the Gradient-Boosted Regression Trees (GBRT) method fed by numerical weather forecasts of the European Centre for Medium Range Weather Forecast (ECMWF) Integrated Forecasting System (IFS) and Ensemble Prediction System (EPS). The proposed methodology is compared with the forecasts obtained with Quantile Regression using only IFS forecasts (QR), with the uncalibrated EPS forecasts and with the EPS forecasts calibrated with a Variance Deficit (VD) procedure. The proposed methodology produces forecasts with a temporal resolution equal to or better than the meteorological forecast (1 h for the IFS and 3 h for EPS) and, in the case examined, is able to provide higher performances than those obtained with the other methods over a forecast horizon of up to 72 h.

The Global Warming Alarm: Forecasts from the Structured Analogies Method

When the beach patrol raises the alarm that a shark has been sighted we know what to do, but how should we respond to an alarm that is based on predictions of what will happen 100 years from now and the person raising the alarm tells us we must make great sacrifices now to avoid the predicted catastrophe? To answer this question, we forecast effects and outcomes of the current global warming alarm using a structured analysis of analogous situations. To do this, we searched the literature and asked experts to identify phenomena that were similar to the alarm currently being raised over dangerous manmade global warming. We obtained 71 proposed analogies. Of these, 26 met our criteria that the alarm be: (1) based on forecasts of human catastrophe arising from effects of human activity on the physical environment, (2) endorsed by experts, politicians and the media, and (3) that were accompanied by calls for strong action. None of the 26 alarms were based on scientific forecasting procedures. None of the alarming forecasts were accurate. Governments took action in 23 of the analogous situations and those actions proved to be harmful in 20. The government programs remained in place after the predicted disasters failed to materialize. The global warming alarm movement appears to be the latest manifestation of a common social phenomenon: false alarms based on unscientific forecasts of human-caused environmental disasters. We predict that the alarm over forecasts of dangerous manmade global warming will, like previous similar alarms, result in harm.

Estimating the Equity Risk Premium Using Implied Cost of Capital

Research on the implied cost of capital (ICC) has found that the equity risk premium is approximately 3%, on average, much lower than estimates based on the mean of historical stock market returns. The validity of such ICC estimates, however, faces both theoretical and empirical challenges. The theoretical equivalence between ICC and expected return requires the stringent condition that the latter is a constant, which is inconsistent with theory and empirical evidence. The empirical estimation of ICC depends on short-run and long-run earnings forecasts, whose biases can severely limit the validity of the estimates. By employing a forecasting procedure that induces little bias in both short-run and long-run forecasts and taking into account the stochastic nature of expected returns, we show that the average market risk premium ranges between 3% and 6%. We also demonstrate that the results of prior research are affected by input errors that are partially self-canceling.

Synoptic thermodynamic and dynamic patterns associated with Quitandinha River flooding events in Petropolis, Rio de Janeiro (Brazil)

Natural disasters are the result of extreme or intense natural phenomena that cause severe impacts on society. These impacts can be mitigated through preventive measures that can be aided by better knowledge of extreme phenomena and monitoring of forecasting and alert systems. The city of Petropolis (in a mountainous region of the state of Rio de Janeiro, Brazil) is prone to heavy rain events, often leading to River overflows, landslides, and loss of life. In that context, this work endeavored to characterize the thermodynamic and dynamic synoptic patterns that trigger heavy rainfall episodes and the corresponding flooding of Quitandinha River. More specifically, we reviewed events from the time period between January 2013 and December 2014 using reanalysis data. We expect that the overall description obtained of synoptic patterns should provide adequate qualitative aid to the decision-making processes involved in operational forecasting procedures. We noticed that flooding events were related to the presence of the South Atlantic Convergence Zone (SACZ), frontal systems (FS), and convective storms (CS). These systems showed a similar behavior on high-frequency wind components, notably with respect to northwest winds before precipitation and to a strong southwest wind component during rainfall events. Clustering analyses indicated that the main component for precipitation formation with regard to CS systems comes from daytime heating, with the dynamic component presenting greater efficiency for the FS configurations. The SACZ events were influenced by moisture availability along the vertical column of the atmosphere and also due to dynamic components of precipitation efficiency and daytime heating, the latter related to the continuous transport of moisture from the Amazon region and South Atlantic Ocean towards Rio de Janeiro state.

Modeling continuous time series with many zeros and an application to earthquakes

We introduce a way to discretize a point process such as earthquakes into time series and propose a class of two‐part autoregressive (2PAR) models for continuous time series data with excess zeros to analyze the dependence structure in such discretized point processes. In the 2PAR models, we employ a Bernoulli variable to model the excess zeros and use autoregressive processes to describe the serial correlation. Using this class of models, we can model correlations that exist in either zeros or nonzeros in the data. We introduce a class of residual analysis to check the goodness‐of‐fit of the proposed models and a forecasting procedure using simulation to check the performance of the models. A simulation study illustrates that the estimators of the proposed models are consistent. Application to the 2010 Darfield earthquake sequence in New Zealand shows that the 2PAR models with serial correlation in both the presence probability and the earthquake stress release provide higher information gain against a reference model.

Real Time Short-term Forecasting Method of Remaining Parking Space in Urban Parking Guidance Systems

Short-term forecasting of the remaining parking space is important for urban parking guidance systems (PGS). The previous methods like polynomial equations and neural network methods are difficult to be applied in practice because of low accuracy or lengthy initial training time which is unfavourable if real-time training is carried out on adapting to changing traffic conditions. To forecast the remaining parking space in real-time with higher accuracy and improve the performances of PGS, this study develops an online forecasting model based on a time series method. By analysing the characteristics of data collected in Nanjing, China, an autoregressive integrated moving average (ARIMA) model has been established and a real-time forecasting procedure developed. The performance of this proposed model has been further analysed and compared with the performances of a neural network method and the Markov chain method. The results indicate that the mean error of the proposed model is about 2 vehicles per 15 minutes, which can meet the requirements for general PGS. Furthermore, this method outperforms the neural network model and the Markov chain method both in individual and collective error analysis. In summary, the proposed online forecasting method appears to be promising for forecasting the remaining parking space in supporting the PGS.

Statistical forecast of seasonal discharge in Central Asia using observational records: development of a generic linear modelling tool for operational water resource management

Abstract. The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien Shan and Pamir and Altai mountains. During the summer months the snow-melt- and glacier-melt-dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydro-meteorological services, this study aims to develop a generic tool for deriving statistical forecast models of seasonal river discharge based solely on observational records. The generic model structure is kept as simple as possible in order to be driven by meteorological and hydrological data readily available at the hydro-meteorological services, and to be applicable for all catchments in the region. As snow melt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature, satellite-based snow cover data, and antecedent discharge. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to four predictors. A user-selectable number of the best models is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross-validation. Based on the cross-validation the predictive uncertainty was quantified for every prediction model. Forecasts of the mean seasonal discharge of the period April to September are derived every month from January until June. The application of the model for several catchments in Central Asia – ranging from small to the largest rivers (240 to 290 000 km2 catchment area) – for the period 2000–2015 provided skilful forecasts for most catchments already in January, with adjusted R2 values of the best model in the range of 0.6–0.8 for most of the catchments. The skill of the prediction increased every following month, i.e. with reduced lead time, with adjusted R2 values usually in the range 0.8–0.9 for the best and 0.7–0.8 on average for the set of models in April just before the prediction period. The later forecasts in May and June improve further due to the high predictive power of the discharge in the first 2 months of the snow melt period. The improved skill of the set of forecast models with decreasing lead time resulted in narrow predictive uncertainty bands at the beginning of the snow melt period. In summary, the proposed generic automatic forecast model development tool provides robust predictions for seasonal water availability in Central Asia, which will be tested against the official forecasts in the upcoming years, with the vision of operational implementation.

Risk forecasting of the femoral head dislocation and its prophylaxis at total hip joint replacement

One of the most frequent complications after total hip replacement is femoral head dislocation. Risk of its forecasting is topical in orthopeadic field.Objective: on the base of retrospective and prospective analysis we wanted to define the quantitative criteria of risk forecasting and to work out the method of dislocation prophylaxis after total hip replacement.Methods: retrospective analysis of clinical and roentgenological examination of 80 patients after total hip replacement was conducted. Neutral cup liner was used. Quantitative criteria of risk forecasting were made with consistent forecast procedure. Prospective study of replacement results was made. We used author’s method — liner with visor — 24 patients (11 men, 13 women, average age (55.0 ± 18.4) years old).Results: the risk factors of the femoral head dislocation are: overweight (82,5 %), muscles weakness in the region of the hip joint (67,5 %), age older than 61 years old (52,5 %), non-compliance of orthopaedic mode in the postoperative period (50,0 %). Computational table was worked out in order to forecast the risk of the femoral head dislocation. We suggest the method of prophylaxis and the treatment with certain position of implant components and the usage of liner with visor.Conclusions: on the base of retrospective analysis of clinical and roentgenological assessment we could reveal the risk factors of femoral head instability after THR. The most significant forecast signs were oversized movements in the hip joint (I = 1.44), femoral head diameter less than 32 mm (I = 0 .50), age older than 61 years old ( I = 0 .47). Favorable result of the surgery was due to 10° cup liner.

Smooth calibration, leaky forecasts, finite recall, and Nash dynamics

We propose to smooth out the calibration score, which measures how good a forecaster is, by combining nearby forecasts. While regular calibration can be guaranteed only by randomized forecasting procedures, we show that smooth calibration can be guaranteed by deterministic procedures. As a consequence, it does not matter if the forecasts are leaked, i.e., made known in advance: smooth calibration can nevertheless be guaranteed (while regular calibration cannot). Moreover, our procedure has finite recall, is stationary, and all forecasts lie on a finite grid. To construct the procedure, we deal also with the related setups of online linear regression and weak calibration. Finally, we show that smooth calibration yields uncoupled finite-memory dynamics in n-person games—“smooth calibrated learning”—in which the players play approximate Nash equilibria in almost all periods (by contrast, calibrated learning, which uses regular calibration, yields only that the time averages of play are approximate correlated equilibria).

Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS

This paper presents a real-time forecasting procedure that utilizes multiple factors with different sampling frequencies to predict the weekly carbon price. Novel combination-MIDAS models with five weight-type schemes are proposed for evaluating the forecast accuracy. The evidence shows that combination-MIDAS models provide forecasting performance gains over traditional models, which supports the use of mixed-frequency data that consist of economic and energy indicators to forecast the weekly carbon price. It is also shown that, Coal is the best predictor for carbon price forecasting and that forecasts that are based on Crude have similar trends to actual carbon prices but are higher than the actual prices.

Spatio-temporal asymmetry of local wind fields and its impact on short-term wind forecasting.

The massive amounts of spatio-temporal data collected in today's wind farms have created a necessity for accurate spatio-temporal models. Despite the growing recognition for non-separable spatio-temporal models, a significant reliance on separable, symmetric models is still the norm in today's renewable industry. We discover that the broad use of separable models is due to the handling of wind data in a setting that does not reveal their fine-scale spatio-temporal structure. The contribution of this research is two-fold. First, we devise a special pair of spatio-temporal "lens" that allows us to see the fine-scale spatio-temporal variations and interactions, and subsequently, we conclude that local wind fields exhibit strong signs of non-separability and asymmetry. Using one year of turbine-specific wind measurements, we show that asymmetry can in fact be detected in more than 93% of the time. Second, making use of the spatio-temporal lens, we propose an enhanced procedure for short-term wind speed forecast. Substantial improvements in forecast accuracy in both wind speed and wind power were observed. When combined with certain intelligent methods such as support vector machine, additional improvements are possible.

Short-term Wave Forecasting using Gaussian Process for Optimal Control of Wave Energy Converters

Wave forecasting is an important prerequisite for implementing optimal control of wave energy converters since the control of power take-off problem is dependent on a reference strategy that must be calculated from a time-varying wave excitation force value. The real-time computation of the wave force excitation is a considerable challenge, depending on predicted values of wave elevation and short-term frequency. In this study, the performance of a Gaussian Process (GP) procedure for short-term wave forecasting is investigated and compared with Neural Network (NN) and Autoregressive modelling (AR) methods. The GP strategy is not only capable of forecasting the mean value of wave elevations but also provides the uncertainty of forecasting, which is beneficial to the safe operation, robust and optimal control of the wave energy converter devices. According to the non-linear and stochastic nature of wave motion, 3 different covariance functions including spectral mixture (SM), quasi-periodic spectral mixture (QPSM) and Matérn (MA) periodic covariance functions are adopted and analyzed in the work. Results indicate the feasibility of using the GP approach in a real application from the viewpoints of high forecasting accuracy and acceptable computational complexity. The forecasting performance comparisons between the AR and NN models consider two dissimilar real wave validation datasets. The overall outcome is that the GP outperforms its counterparts. However, prediction of the uncertainty is a strong contribution compared with other approaches.

Синтез управления маневром уклонения беспилотного летательного аппарата с учетом терминальных ограничений

Introduction. The solution to the problem of development of suboptimal evasive strategy of unmanned aerial vehicle (UAV) under the conditions of uncertainty of the current motion characteristics, and the future behavior strategy of the interdictor is considered. The FV (flying vehicle) control is organized with maximum efficiency. Herewith, the maneuvering FV trajectory must satisfy the requirements of optimality for some given functions of the phase variables at the final time and the conditions of its path through a specified terminal area space. The initial conditions and the dynamic facilities of the evading and opposing FV at the initial time are assumed to be known. The evading FV control is developed by its onboard guidance and navigation system in real time on nonlinear position feedback control. The work objective is to build an algorithm for solving the problem of synthesis of terminal optimal control of motion of center of mass of highspeed UAV that allows searching for the optimal control in function of the current coordinates of the object. Consideration of the problem in this formulation distinguishes it from the classical conflict tasks on the prosecution to be solved with the involvement of the theory of differential games, and demands computationally efficient solution methods. Materials and Methods. For the UAV operating under the conditions of violent disturbances, the most effective hard constraints on the phase variables and control are the so-called methods of optimal terminal control that implement adaptive algorithms with projection. Therefore, the solution to the formulated problem can be obtained precisely with the use of their ideology. Research Results. A new method of piecewise-software terminal control of the UAV motion is developed. It differs from the known ones in that it is based on the procedure of searching a control strategy upon the best guaranteed result with the terminal restrictions. A methodology for calculating the management strategy for the FV evasive maneuver in a slow loop of the terminal control system is worked out. It is characterized in that instead of the forecast procedure in speed-up time and calculation of residuals, the conversion of a boundary value problem to a one-point task of integration of the ordinary differential equations system with account for the occurrence of non-zero terminal member in the cost functional, is used. Discussion and Conclusions. Despite the fact that the development of a suboptimal guidance strategy in the terminal area of the evading FV is not in the form of synthesis, the control software adapts to the current conditions. This is achieved through the iterative procedures of regular recalculation of terminal conditions that are equivalent to the periodic circuit feedback. Thus, the evading FV is brought to a specified terminal area through software, while evading an interdictor is developed as synthesis. Limitations to the method are the terms that the movement model of the opposing FV is known, and it forms a natural control of the evader intercept.

A review of Zimbabwe’s macro-fiscal forecasts

Purpose The purpose of this paper is to evaluate accuracy of macro fiscal forecasts done by Government of Zimbabwe and the spillover effects of forecasting errors over the period 2010-2015. Design/methodology/approach In line with the study objectives, the study employed the root mean square error methodology to measure the accuracy of macro fiscal forecasts, borrowing from the work of Calitz et al. (2013). The spillover effects were assessed through running simple regression in Eviews programme. The data used in the analysis are based on annual national budget forecasts presented to the Parliament by the Minister of Finance. Actual data come from the Ministry of Finance budget outturns and Zimbabwe Statistical Agency published national accounts. Findings The results of the root mean square error revealed relatively high levels of macro-fiscal forecasting errors, with revenue recording the highest. The forecasting errors display a tendency of under predicting the strength of economic recovery during boom and over predicting its strength during periods of weakness. The study although found significant evidence of GDP forecasting errors translating into revenue forecasting inaccuracies, the GDP forecasting errors fail to fully account for the revenue errors. Revenue errors were, however, found to be positive and significant in explaining the budget balance errors. Originality/value In other jurisdictions, particularly developed countries, they undertake regular evaluation of their forecasts in order to improve their forecasting procedures, which translate into quality public service delivery. The situation is lagging in Zimbabwe. Given the poor performance in public service delivery in Zimbabwe, this study contributes in dissecting the sources of the challenge by providing a comprehensive review of macro fiscal forecasts.

Modelling Air Passenger Traffic Flow in Murtala Muhammad International Airport Lagos, Nigeria: A Time Series Approach

In this study, Artificial Neural Network (ANN), Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and Holt-Winters Exponential Smoothing (HWES) models are used to model air passenger traffic flow in Murtala Muhammad International Airport (MMIA), Lagos Nigeria. The performances of these proposed models are compared for in-sample and out-of-sample performance by employing static forecast procedure over January 2014 to December 2015 forecast horizon. The best models from the SARIMA, ANN and HWES were selected by employing some performance metrics comprising, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) and residual diagnostics. The selected models forecasting performances were compared using the statistical loss functions, Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) for the measurement of forecast accuracy. Results show that ANN outperforms the other models in the domestic sector, while the HWES had the best performance in the international sector even though it was outperformed by SARIMA in the domestic sector. ANN yielded the best in-sample performance for domestic and international air passenger traffic. It was concluded that the ANN, which represents a class of non-linear time series model is very efficient in mimicking time series pattern and giving good forecast.