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The usage of formal statistical forecasting procedures has been shown in numerous studies to improve forecast accuracy and, consequently, organizational performance. However, the process of implementing and managing this technology can run into many stumbling blocks. Identifies six major organizational problems when implementing and developing formal statistical forecasting procedures. Provides solution strategies to these problems and discusses specific managerial implications. This information is important to managers in order to gain the greatest benefit from the forecasting function.

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.

In recent times there has been a change of emphasis in business forecasting. The shift has been away from the technical and statistical aspects. More thought is now being given to the way in which techniques are used and the context in which they are applied. This article is the first in a series of two which deal with these issues. It describes the role of the manager in forecasting. In particular, it discusses the tasks in designing and planning a forecasting system which are the key to its success and which fall within a manager's responsibility. The second article is concerned with the link between forecasts and the decisions they support.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

Commercial real estate and office rental values, in particular, have long been the focus of research. Several forecasting frameworks for office rental values in multivariate and univariate fashions have been proposed. Recent developments in time series forecasting using machine learning and deep learning methods offer an opportunity to update traditional univariate forecasting frameworks.

With the aim to extend research on univariate rent forecasting a hybrid methodology combining both ARIMA and a neural network model is proposed to exploit the unique strengths of both methods in linear and nonlinear modelling. N-BEATS, a deep learning algorithm that has demonstrated state-of-the-art forecasting performance in major forecasting competitions, are explained. With the ARIMA model, it is jointly applied to the office rental dataset to produce forecasts for four-quarters ahead.

When the approach is applied to a dataset of 21 major European office cities, the results show that the ensemble model can be an effective approach to improve the prediction accuracy achieved by each of the models used separately.

Real estate forecasting is essential for assessing the value of managing portfolios and for evaluating investment strategies. The approach applied in this paper confirms the heterogeneity of real estate markets. The application of mixed modelling via linear and nonlinear methods decreases the uncertainty of abrupt changes in rents.

To the best of the authors' knowledge, no such application of a hybrid model updating classical statistical forecasting with a deep learning neural network approach in the field of commercial real estate rent forecasting has been undertaken.

Explores the relationships between statistical process control (SPC) and forecasting procedures. While both procedures are often applied and used in different contexts, a careful analysis shows that they go through the same stages that culminate in process or forecast monitoring. This apparent similarity of SPC and forecasting enables a general framework to be established for model‐based SPC. Discusses some forecasting procedures applicable to SPC and underlines the importance of SPC concepts in forecasting.

The purpose of this paper, because of the complexity of demand time series and the need to construct a more accurate hybrid model that can model all relationships in data, is to propose a parallel-series hybridization of seasonal neural networks and statistical models for demand time series forecasting.

The main idea of proposed model is centered around combining parallel and series hybrid methodologies to use the benefit of unique advantages of both hybrid strategies as well as intelligent and classic seasonal time series models simultaneously for achieving results that are more accurate for the first time. In the proposed model, in contrast of traditional parallel and series hybrid strategies, it can be generally shown that the performance of the proposed model will not be worse than components.

Empirical results of forecasting two well-known seasonal time series data sets, including the total production value of the Taiwan machinery industry and the sales volume of soft drinks, indicate that the proposed model can effectively improve the forecasting accuracy achieved by either of their components used in isolation. In addition, the proposed model can achieve more accurate results than parallel and series hybrid model with same components. Therefore, the proposed model can be used as an appropriate alternative model for seasonal time series forecasting, especially when higher forecasting accuracy is needed.

To the best of the authors’ knowledge, the proposed model, for first time and in contrast of traditional parallel and series hybrid strategies, is developed.

Focuses on the implementation of forecasting systems and processes by large organizations. Reports the results of a survey of US firms which reveal that, despite advances in computer technology, judgemental forecasting continues to be the method managers prefer. Notes, however, that the incorporation of total quality practices appears to be having some impact on improving systematic approaches to forecasting.

In order to predict the future we must examine the past in order to observe trends over periods of time and establish the degree of probability with which these trends are likely to repeat themselves in the future. All forecasts are wrong, and management must be aware of this fact and decide upon the degree of inexactitude that can be tolerated when planning for the future.

FORECASTING is not just a specialized management technique, but something which we all understand and do quite naturally. Because of the existence of time, all of us run our lives by making forecasts. In the present (which is an infinitesimally short period of time) we have to make forecasts of what will happen in the future based upon our experience of the past. Without such forecasts, we could not run our lives because we have to keep making decisions about the future.