Judgement in learning‐curve forecasting: a laboratory study

This study investigates whether human judgement can be of value to users of industrial learning curves, either alone or in conjunction with statistical models. In a laboratory setting, it compares the forecast accuracy of a statistical model and judgemental forecasts, contingent on three factors: the amount of data available prior to forecasting, the forecasting horizon, and the availability of a decision aid (projections from a fitted learning curve). The results indicate that human judgement was better than the curve forecasts overall. Despite their lack of field experience with learning curve use, 52 of the 79 subjects outperformed the curve on the set of 120 forecasts, based on mean absolute percentage error. Human performance was statistically superior to the model when few data points were available and when forecasting further into the future. These results indicate substantial potential for human judgement to improve predictive accuracy in the industrial learning‐curve context. Copyright © 1999 John Wiley & Sons, Ltd.     

Short‐term forecasting of industrial electricity consumption in Brazil

This paper presents short‐term forecasting methods applied to electricity consumption in Brazil. The focus is on comparing the results obtained after using two distinct approaches: dynamic non‐linear models and econometric models. The first method, that we propose, is based on structural statistical models for multiple time series analysis and forecasting. It involves non‐observable components of locally linear trends for each individual series and a shared multiplicative seasonal component described by dynamic harmonics. The second method, adopted by the electricity power utilities in Brazil, consists of extrapolation of the past data and is based on statistical relations of simple or multiple regression type. To illustrate the proposed methodology, a numerical application is considered with real data. The data represents the monthly industrial electricity consumption in Brazil from the three main power utilities: Eletropaulo, Cemig and Light, situated at the major energy‐consuming states, Sao Paulo, Rio de Janeiro and Minas Gerais, respectively, in the Brazilian Southeast region. The chosen time period, January 1990 to September 1994, corresponds to an economically unstable period just before the beginning of the Brazilian Privatization Program. Implementation of the algorithms considered in this work was made via the statistical software S‐PLUS. Copyright © 1999 John Wiley & Sons, Ltd.     

Predicting nominal variable relationships with multiple response

For forecasting purposes, it is useful to predict the most likely response of an individual to a nominally-scaled variable using the response to a predictor variable which is also nominally scaled. Traditional statistical approaches are not suitable when respondents provide multiple responses. For practical applications it is desirable to provide a simple measure of prediction that is easy to calculate and understand. Two situations are described where predictions of multiple response are implemented and two indices of predictive association are developed for the situations. These indices provide predictive explanations where none were possible using traditional methods of predictive association. The need to complement these indices with conditional probabilities and log-linear models is suggested. The evaluation and implications of these indices are discussed.     

Predictive ability and economic gains from volatility forecast combinations

The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms.     

Are industry‐level indicators more helpful to forecast industrial stock volatility? Evidence from Chinese manufacturing purchasing managers index

Effectively explaining and accurately forecasting industrial stock volatility can provide crucial references to develop investment strategies, prevent market risk and maintain the smooth running of national economy. This paper aims to discuss the roles of industry‐level indicators in industrial stock volatility. Selecting Chinese manufacturing purchasing managers index (PMI) and its five component PMI as the proxies of industry‐level indicators, we analyze the contributions of PMI on industrial stock volatility and further compare the volatility forecasting performances of PMI, macroeconomic fundamentals and economic policy uncertainty (EPU), by constructing the individual and combination GARCH‐MIDAS models. The empirical results manifest that, first, most of the PMI has significant negative effects on industrial stock volatility. Second, PMI which focuses on the industrial sector itself is more helpful to forecast industrial stock volatility compared with the commonly used macroeconomic fundamentals and economic policy uncertainty. Finally, the combination GARCH‐MIDAS approaches based on DMA technique demonstrate more excellent predictive abilities than the individual GARCH‐MIDAS models. Our major conclusions are robust through various robustness checks.     

Stock index prediction based on wavelet transform and FCD-MLGRU

With the development of artificial intelligence, deep learning is widely used in the field of nonlinear time series forecasting. It is proved in practice that deep learning models have higher forecasting accuracy compared with traditional linear econometric models and machine learning models. With the purpose of further improving forecasting accuracy of financial time series, we propose the WT-FCD-MLGRU model, which is the combination of wavelet transform, filter cycle decomposition and multilag neural networks. Four major stock indices are chosen to test the forecasting performance among traditional econometric model, machine learning model and deep learning models. According to the result of empirical analysis, deep learning models perform better than traditional econometric model such as autoregressive integrated moving average and improved machine learning model SVR. Besides, our proposed model has the minimum forecasting error in stock index prediction.     

Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank

The main focus of this paper is to model the daily series of banknotes in circulation. The series of banknotes in circulation displays very marked seasonal patterns. To the best of our knowledge the empirical performance of two competing approaches to model seasonality in daily time series, namely the ARIMA‐based approach and the Structural Time Series approach, has never been put to the test. The application presented in this paper provides valid intuition on the merits of each approach. The forecasting performance of the models is also assessed in the context of their impact on the liquidity management of the Eurosystem. Copyright © 2008 John Wiley & Sons, Ltd.     

Can panel data really improve the predictability of the monetary exchange rate model?

A common explanation for the inability of the monetary model to beat the random walk in forecasting future exchange rates is that conventional time series tests may have low power, and that panel data should generate more powerful tests. This paper provides an extensive evaluation of this power argument to the use of panel data in the forecasting context. In particular, by using simulations it is shown that although pooling of the individual prediction tests can lead to substantial power gains, pooling only the parameters of the forecasting equation, as has been suggested in the previous literature, does not seem to generate more powerful tests. The simulation results are illustrated through an empirical application. Copyright © 2007 John Wiley & Sons, Ltd.     

Short term load forecasting in electric power systems: A comparison of ARMA models and extended wiener filtering

On-line prediction of electric load in the buses of the EHV grid of a power generation and transmission system is basic information required by on-line procedures for centralized advanced dispatching of power generation. This paper presents two alternative approaches to on-line short term forecasting of the residual component of the load obtained after the removal of the base load from a time series of total load. The first approach involves the use of stochastic ARMA models with time-varying coefficients. The second consists in the use of an extension of Wiener filtering due to Zadeh and Ragazzini. Real data representing a load process measured in an area of Northern Italy and simulated data reproducing a non-stationary process with known characteristics constitute the basis of a numerical comparison allowing one to determine under which conditions each method is more appropriate.     

ARARMA models for time series analysis and forecasting

Methods of time series forecasting are proposed which can be applied automatically. However, they are not rote formulae, since they are based on a flexible philosophy which can provide several models for consideration. In addition it provides diverse diagnostics for qualitatively and quantitatively estimating how well one can forecast a series. The models considered are called ARARMA models (or ARAR models) because the model fitted to a long memory time series (t) is based on sophisticated time series analysis of AR (or ARMA) schemes (short memory models) fitted to residuals Y(t) obtained by parsimonious‘best lag’non-stationary autoregression. Both long range and short range forecasts are provided by an ARARMA model Section 1 explains the philosophy of our approach to time series model identification. Sections 2 and 3 attempt to relate our approach to some standard approaches to forecasting; exponential smoothing methods are developed from the point of view of prediction theory (section 2) and extended (section 3). ARARMA models are introduced (section 4). Methods of ARARMA model fitting are outlined (sections 5,6). Since‘the proof of the pudding is in the eating’, the methods proposed are illustrated (section 7) using the classic example of international airline passengers.     

Analysis of the relationship between LSTM network traffic flow prediction performance and statistical characteristics of standard and nonstandard data

The effectiveness of road traffic control systems can be increased with the help of a model that can accurately predict short-term traffic flow. Therefore, the performance of the preferred approach to develop a prediction model should be evaluated with data sets with different statistical characteristics. Thus a correlation can be established between the statistical properties of the data set and the model performance. The determination of this relationship will assist experts in choosing the appropriate approach to develop a high-performance short-term traffic flow forecasting model. The main purpose of this study is to reveal the relationship between the long short-term memory network (LSTM) approach's short-term traffic flow prediction performance and the statistical properties of the data set used to develop the LSTM model. In order to reveal these relationships, two different traffic prediction models with LSTM and nonlinear autoregressive (NAR) approaches were created using different data sets, and statistical analyses were performed. In addition, these analyses were repeated for nonstandardized traffic data indicating unusual fluctuations in traffic flow. As a result of the analyses, LSTM and NAR model performances were found to be highly correlated with the kurtosis and skewness changes of the data sets used to train and test these models. On the other hand, it was found that the difference of mean and skewness values of training and test sets had a significant effect on model performance in the prediction of nonstandard traffic flow samples.     

Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices

Improving the prediction accuracy of agricultural product futures prices is important for investors, agricultural producers, and policymakers. This is to evade risks and enable government departments to formulate appropriate agricultural regulations and policies. This study employs the ensemble empirical mode decomposition (EEMD) technique to decompose six different categories of agricultural futures prices. Subsequently, three models—support vector machine (SVM), neural network (NN), and autoregressive integrated moving average (ARIMA)—are used to predict the decomposition components. The final hybrid model is then constructed by comparing the prediction performance of the decomposition components. The predicting performance of the combination model is then compared with the benchmark individual models: SVM, NN, and ARIMA. Our main interest in this study is on short-term forecasting, and thus we only consider 1-day and 3-day forecast horizons. The results indicate that the prediction performance of the EEMD combined model is better than that of individual models, especially for the 3-day forecasting horizon. The study also concluded that the machine learning methods outperform the statistical methods in forecasting high-frequency volatile components. However, there is no obvious difference between individual models in predicting low-frequency components.     

Power grid operation optimization and forecasting using a combined forecasting system

Reliable photovoltaic and wind power generation forecasts are essential for efficient power systems operations. A combined forecasting system is developed, which integrates a data preprocessing method, a sub-predictor selection rule, and a multi-objective optimization to integrate various forecasting models. The proposed system effectively aggregates the advantages of all algorithms involved, facilitating greater prediction precision and stability. Experiments indicated that the proposed system can achieve higher quality point and interval forecasting performance relative to the comparative approaches.     

Price forecasting and evaluation: An application in agriculture

Because of the high volatility of prices of agricultural commodities over the past decade, the importance of accurate price forecasting for decision makers has become even more acute. This paper reviews literature on forecasting and evaluation. An application with forecasting U.S. hog prices is presented which includes both economic and statistical evaluation measures. Seven forecasting approaches are described and their performances are examined over 24 quarters from 1976 to 1981. These methods include exponential smoothing, an autoregressive integrated moving average process, an econometric model, expert judgement, and a composite forecasting approach. The application gives results which support previous findings in the forecasting literature and suggests that forecasting methods can provide valuable information to the decision maker.     

Forecasting Temperature Indices Density with Time‐Varying Long‐Memory Models

The hedging of weather risks has become extremely relevant in recent years, promoting the diffusion of weather‐derivative contracts. The pricing of such contracts requires the development of appropriate models for the prediction of the underlying weather variables. Within this framework, a commonly used specification is the ARFIMA‐GARCH. We provide a generalization of such a model, introducing time‐varying memory coefficients. Our model satisfies the empirical evidence of the changing memory level observed in average temperature series, and provides useful improvements in the forecasting, simulation, and pricing issues related to weather derivatives. We present an application related to the forecast and simulation of a temperature index density, which is then used for the pricing of weather options. Copyright © 2011 John Wiley & Sons, Ltd.     

Modeling of frequency containment reserve prices with econometrics and artificial intelligence

The forecasting of prices for electricity balancing reserve power can essentially improve the trading positions of market participants in competitive auctions. Having identified a lack of literature related to forecasting balancing reserve prices, we deploy approaches originating from econometrics and artificial intelligence and set up a forecasting framework based on autoregressive and exogenous factors. We use SARIMAX models as well as neural networks with different structures and forecast based on a rolling one-step forecast with reestimation of the models. It turns out that the naive forecast performs reasonably well but is outperformed by the more advanced models. In addition, neural network approaches outperform the econometric approach in terms of forecast quality, whereas for the further use of the generated models the econometric approach has advantages in terms of explaining price drivers. For the present application, more advanced configurations of the neural networks are not able to further improve the forecasting performance.     

Bootstrap prediction bands for forecast paths from vector autoregressive models

The problem of forecasting from vector autoregressive models has attracted considerable attention in the literature. The most popular non‐Bayesian approaches use either asymptotic approximations or bootstrapping to evaluate the uncertainty associated with the forecast. The practice in the empirical literature has been to assess the uncertainty of multi‐step forecasts by connecting the intervals constructed for individual forecast periods. This paper proposes a bootstrap method of constructing prediction bands for forecast paths. The bands are constructed from forecast paths obtained in bootstrap replications using an optimization procedure to find the envelope of the most concentrated paths. From extensive Monte Carlo study, it is found that the proposed method provides more accurate assessment of predictive uncertainty from the vector autoregressive model than its competitors. Copyright © 2010 John Wiley & Sons, Ltd.     

ARMA Models and the Box–Jenkins Methodology

The purpose of this paper is to apply the Box–Jenkins methodology to ARIMA models and determine the reasons why in empirical tests it is found that the post-sample forecasting the accuracy of such models is generally worse than much simpler time series methods. The paper concludes that the major problem is the way of making the series stationary in its mean (i.e. the method of differencing) that has been proposed by Box and Jenkins. If alternative approaches are utilized to remove and extrapolate the trend in the data, ARMA models outperform the models selected through Box–Jenkins methodology. In addition, it is shown that using ARMA models to seasonally adjusted data slightly improves post-sample accuracies while simplifying the use of ARMA models. It is also confirmed that transformations slightly improve post-sample forecasting accuracy, particularly for long forecasting horizons. Finally, it is demonstrated that AR(1), AR(2) and ARMA(1,1) models can produce more accurate post-sample forecasts than those found through the application of Box–Jenkins methodology.© 1997 John Wiley & Sons, Ltd.     

Model uncertainty and forecast accuracy

In time-series analysis, a model is rarely pre-specified but rather is typically formulated in an iterative, interactive way using the given time-series data. Unfortunately the properties of the fitted model, and the forecasts from it, are generally calculated as if the model were known in the first place. This is theoretically incorrect, as least squares theory, for example, does not apply when the same data are used to formulates and fit a model. Ignoring prior model selection leads to biases, not only in estimates of model parameters but also in the subsequent construction of prediction intervals. The latter are typically too narrow, partly because they do not allow for model uncertainty. Empirical results also suggest that more complicated models tend to give a better fit but poorer ex-ante forecasts. The reasons behind these phenomena are reviewed. When comparing different forecasting models, the BIC is preferred to the AIC for identifying a model on the basis of within-sample fit, but out-of-sample forecasting accuracy provides the real test. Alternative approaches to forecasting, which avoid conditioning on a single model, include Bayesian model averaging and using a forecasting method which is not model-based but which is designed to be adaptable and robust.     

Why do EMD‐based methods improve prediction? A multiscale complexity perspective

Empirical mode decomposition (EMD)‐based ensemble methods have become increasingly popular in the research field of forecasting, substantially enhancing prediction accuracy. The key factor in this type of method is the multiscale decomposition that immensely mitigates modeling complexity. Accordingly, this study probes this factor and makes further innovations from a new perspective of multiscale complexity. In particular, this study quantitatively investigates the relationship between the decomposition performance and prediction accuracy, thereby developing (1) a novel multiscale complexity measurement (for evaluating multiscale decomposition), (2) a novel optimized EMD (OEMD) (considering multiscale complexity), and (3) a novel OEMD‐based forecasting methodology (using the proposed OEMD in multiscale analysis). With crude oil and natural gas prices as samples, the empirical study statistically indicates that the forecasting capability of EMD‐based methods is highly reliant on the decomposition performance; accordingly, the proposed OEMD‐based methods considering multiscale complexity significantly outperform the benchmarks based on typical EMDs in prediction accuracy.