Measures Of Forecast Accuracy Research Articles

Modelling and forecasting European Airbnb occupancy during the pandemic: The specific merits of panel-data and Markov-switching models

This study models European Airbnb occupancy from January 2017 to December 2020 for 43 European countries within an econometric panel-data framework. Besides typical economic covariates, the COVID-19 Stringency Index is employed to allow for the recent pandemic. The counterfactual observations created by two versions of the econometric model (one with and one without European aggregates of macroeconomic controls) are benchmarked against traditional univariate time-series models and a seasonal Markov-switching autoregression. For pandemic times, the latter performs very well in terms of several forecast accuracy measures due to its ability of (a) detecting the pandemic-induced structural break and (b) accounting for seasonal patterns in the data. The econometric models also predict well as they outperform the time-series models in the majority of cases. Hence, the panel-data approach is suitable when looking for economically interpretable Airbnb occupancy forecasts, even during the time of the pandemic.

Forecasting of Monthly Crude Oil Prices in Kenya Using Comparative Time Series Models

Crude oil is one of the most vital products that ever existed and variation in prices affects all sectors of the economy and variation in its prices is very crucial. Therefore, without an accurate and appropriate predictive model for crude oil prices, it has proven difficult to predict future oil prices. Therefore, appropriate modeling is crucial for the oil companies to adjust strategies used in the production and supply as well as structural optimization. This study sought to fit several time series forecasting models namely; ARIMA, Naïve, Seasonal Naïve, Time Series Linear Model (TSLM), and Exponential Smoothing (ETS) to model and forecast crude oil prices. The study used Kenya’s monthly crude oil prices from Jan 2003 to Dec 2023, giving a sample of 252 observations. The selection of the best model was based on the minimization of the information criteria, where ARIMA, ETS, and Seasonal Naïve attained an AIC of 1294.193, 1846.780, and 1403.821, respectively. Similarly, the models attained the BIC of 1304.991, 1863.071, and 1450.489, respectively. Since the Naïve and TSLM could not provide the AIC and BIC, model selection was entirely based on the forecast accuracy measures. From the results, the Naive model reported the lowest RMSE (19.6329), indicating that it has the smallest average squared error, with the lowest MAE (15.5212), suggesting it has the smallest average absolute error. Besides, the Naive model reported an ME (1.9819) which is relatively low but not the lowest. The automatic ETS model has a slightly lower ME (3.478211). The naive model reported lower MPE and MAPE values (-3.9525 and 26.8022, respectively) compared to most models, indicating less percentage error. Similarly, a lower MASE and RMSSE were reported by the naïve model, (0.7343624) and RMSSE (0.7228792), respectively, indicating that the model performs well relative to forecasting crude oil prices in Kenya. The naïve model demonstrated a higher consistency and reliability in forecasting crude oil prices in Kenya.

Using ARIMA Model to Forecast Electricity Load in Jordan

Objectives: This study aims to forecast the daily peak electricity load in Jordan using a dataset of hourly peak load data for the period from January 1, 2010, to December 31, 2022, compiled by the National Electric Power Company (NEPCO). Methods: This study employs the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to make forecasts. The data exhibits an upward trend, seasonality, and non-constant variance. To address these features, the SARIMA model is used to account for the trend and seasonality, while a Box-Cox transformation is applied to manage the non-constant variance. Results: Following the standard Box-Jenkins methodology (identification, estimation, diagnostic checking, and forecasting) and utilizing the “(auto.arima)” function in the RStudio software package, the resulting SARIMA model is ARIMA(1,0,1)(2,1,2)[7]. This model is used to forecast 7 future values of the electricity load. The Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) values, as measures of forecast accuracy, support the precision of our forecasts. Conclusion: Based on empirical results, electricity companies in Jordan are encouraged to use time series models for forecasting electricity loads instead of relying on simple spreadsheet models.

Forecasting mail flow: A hierarchical approach for enhanced societal wellbeing

Forecasting for Social Good has gained considerable attention for its impact on individuals, businesses, and society. This research introduces an integrated hierarchical forecasting-based decision-making approach for mail flow in a major postal organisation, presenting new social performance indicators. These indicators, including the discharge level, discharge rate, and overload rate, guide decision makers toward consistent workload planning, bridging a literature gap concerning forecast utility measures. The study evaluates three forecasting methods—exponential smoothing with error, trend, and seasonality (ETS), the autoregressive integrated moving average (ARIMA), and the light gradient boosting machine (LightGBM)—in terms of forecast accuracy and social measures, comparing them to the organisation’s current method. The empirical results confirm that the proposed approach is more accurate than the current method. Moreover, while ETS shows the highest forecast accuracy, LightGBM outperforms all methods in social measures. This indicates that a highly accurate forecasting method does not always enhance social performance, challenging traditional views on forecasting evaluation.

Nonnegative GARCH-type models with conditional Gamma distributions and their applications

Most of real data are characterized by positive, asymmetric and skewed distributions of various shapes. Modelling and forecasting of such data are addressed by proposing nonnegative conditional heteroscedastic time series models with Gamma distributions. Three types of time-varying parameters of Gamma distributions are adopted to construct the nonnegative GARCH models. A condition for the existence of a stationary Gamma-GARCH model is given. Parameter estimates are discussed via maximum likelihood estimation (MLE) method. A Monte-Carlo study is conducted to illustrate sample paths of the proposed models and to see finite-sample validity of the MLEs, as well as to evaluate model diagnostics using standardized Pearson residuals. Furthermore, out-of-sample forecasting analysis is performed to compute forecasting accuracy measures. Applications to oil price and Bitcoin data are given, respectively.

Estimating restaurants’ unconstrained demand: a systematic approach to reducing structural bias in forecast accuracy measures

研究目的技术从其他行业的传播以及厨房设备的创新推动了餐饮业内的结构变化。然而, 这种变化直接影响了评估需求预测准确性。本研究探讨了在餐饮业结构改变后，评估至关重要的需求预测准确性时所面临的令人独特和复杂性。研究方法本文自研了一个数学模型来描述和探讨评估需求预测准确性中的结构性偏差的本质。然后, 使用数值模拟构建一个市场示例, 以更好地了解上述偏差的特征。最后, 将这种预测准确性评估的系统性偏差与其他传统的餐饮业需求预测情境进行对比。研究发现本文概述了中央厨房运营中需求预测是动态的, 因此产生了结构性偏差的理论基础。更具体地说, 在使用中央厨房并集中订单的情境下, 本文发现需求预测直接设定了容量限制, 因此产生了在需求预测准确度衡量中的结构性偏差。依赖这样的预测准确性度量可能产生严重的负面商业结果。研究创新这项研究首次表明, 在中央厨房运营的独特的新环境中, 由于新的设定即每日菜品需求预测直接决定每日容量水平, 需求预测准确度衡量标准有着严重偏差, 长期来讲准确性可能下降, 从而导致次优的商业决策。本研究的主要理论贡献是提供一个餐饮企业在新运营环境中解释和描述需求预测准确度中结构性偏差的全新分析模型。

A time series analysis of monthly issuance of plant import permits in Nigeria

An import permit is issued by the National Plant Protection Organization of each country, known in Nigeria as the Nigeria Agricultural Quarantine Service (NAQS), to allow the importation of pest-free plants and prevent the introduction of quarantine pests or check the entry of regulated non-quarantine pests of imported plant commodities. Issuance of import permits at the Post-Entry Quarantine, Surveillance and Diagnostic Station (PEQDS), Ibadan is mostly for germplasm materials mainly used for cultivation, which poses a high risk of pest introduction. Thus, this study investigated the statistical components of the issuance of import permits at the PEQDS using the Time Series approach to determine the peak period of collection essential for planning phytosanitary activities to mitigate the introduction of foreign pests. The dataset of the monthly issuance of import permits from January 2012 to July 2022 was analysed. From the preliminary study, we observed that the series exhibit seasonal and stationary components with no definite trend pattern, hence we modelled the series using a Seasonal Autoregressive Moving Average (SARIMA) model. Several models were estimated and the best model (SAR(12)) for forecasting the future import permit collectionwas selected based on the forecast accuracy measures. The forecasted values indicated the highest issuance of import permits in May and September, each year and showed a fluctuating movement over time in the future. To further prevent pest incursion and facilitate trade, NAQS needs to operate round-the-clock surveillance, keep its staff on high alert and adequate facilities operational for more effective activities. Other factors responsible for low and fluctuating issuance of import permits should also be investigated.

Econometric Modeling and Forecasting of Arabica and Robusta Coffee Production for Sustainable Agriculture Development

In the present study, we have used Box-Jenkins approaches an Autoregressive Integrated Moving Average model (ARIMA) for modeling and forecasting of annual amount of Arabica and Robusta coffee production and yield (ARCPY) in India. In this study used time series data was collected from the official website of the coffee board of India from 1986 to 2023 (38 observations). Augmented Dickey-Fuller (ADF) test has used for testing the stationarity of the time series, and the appropriate ARIMA model has selected based on minimum Akaike Information Criterion (AIC). The ARIMA models has compared with the other ARIMA models with respect to forecast accuracy measures, and the residuals has diagnosed for possible presence of autocorrelation, and white noise heteroscedasticity (WNH) test of the fitted models. The MAPE value of ACP and ACY has 8.94 and 9.35 percent respectively shows highly accurate forecasting percentage rate respectively. While, the MAPE value of RCP and RCY has 16.03 and 11.43 percent respectively shows good accurate forecasting percentage rate respectively. Thus, we found the ARIMA (2, 1, 4), (3, 1, 2), (0, 1, 3) and (2, 0, 1) models for Arabica and Robusta coffee; which has observed as the best suitable model for predicting the future annual amount of Arabica coffee production (ACP), Arabica coffee yield (ACY), Robusta coffee production (RCP) and Robusta coffee yield (RCY) respectively, and we have estimated that the annual amount of ACP and ACY achieved in the year 2023-24 from 97379.67 MTs, and 472.29 kg/hectare respectively to 93272.91 MTs, and 379.31 kg/hectare respectively in the year 2034-35 will decrease, and the annual amount of RCP and RCY achieved in the year 2023-24 from 268655.21 MTs, and 1110.68 kg/hectare respectively to 318614.85 MTs, and 1012.90 kg/hectare respectively in the year 2034-35 will reach.

Proposing a Hybrid Method (Artificial Neural Networks – ARIMA) to Predict Temperatures in the City of Baghdad

Statistical methods and artificial intelligence play an important role in building models that can predict time series and thus help in developing strategic plans. The study aimed to propose a hybrid model (ARIMA and artificial neural networks) to predict the temperature in the city of Baghdad and compare it with the AA and N models based on the temperatures in the city of Baghdad for the period from January 2017 to December 2021. By comparing these models using forecast accuracy measures MAE, MSE, and MAPE, it was found that the hybrid method gives lower error values than ARIMA models and artificial neural networks, as it addresses the problem of non-linearity in the data, so it was relied upon to predict temperatures in the city of Baghdad for the next six months.

A Study of Nigeria Monthly Stock Price Index Using ARTFIMA-FIGARCH Hybrid Model

Long memory is a phenomenon in time series analysis that is exhibited by a slow decay of the autocorrelation function. It has been observed that the presence of long memory in both mean and volatility can complicate model fitting and compromise forecasting reliability. Meanwhile, the Autoregressive Tempered Fractional Integrated Moving Average (ARTFIMA) as a tempered fractionally differenced long memory mean model and the Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity (FIGARCH), which is a long memory variance model, could not independently and effectively address the challenges of time series data that displayed long memory in mean and volatility. To tackle this challenge, we introduce a hybrid model called the ARTFIMA-FIGARCH by combining ARTFIMA and FIGARCH models using the transformation method under the assumption that the residuals of the ARTFIMA model are non-normal, serially correlated, and heteroscedastic. To evaluate the effectiveness of this model, we employed the Nigerian Monthly Stock Price Index as well as simulated data sets as a testing ground and compared its performance against existing models like ARFIMA, ARTFIMA, and ARFIMA-FIGARCH. The selection of the most suitable model was determined using the Akaike Information Criterion (AIC) and model performance was assessed through various forecast accuracy measures. Our findings demonstrated that ARTFIMA (0,1.06,1)-FIGARCH (1,0.15,1) emerged as the best candidate of the new model and outperformed ARFIMA (1,1.06,0)-FIGARCH (1,0.15,1). Based on the findings of this study, it is concluded that ARTFIMA-FIGARCH is considered to be the most suitable model for studying the mean and volatility of the Nigerian monthly stock price index.

GARCH Models and Distributions Comparison for Nonlinear Time Series with Volatilities

The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is extensively used for handling volatilities. However, with numerous extensions to the standard GARCH model, selecting the most suitable model for forecasting price volatilities becomes challenging. This study aims to examine the performance of different GARCH models in forecasting crude oil price volatilities using West Texas Intermediate (WTI) data. The models considered are the standard GARCH, Integrated GARCH (IGARCH), Exponential GARCH (EGARCH), and Golsten, Jagannathan, and Runkle GARCH (GJR-GARCH), each with normal distribution, Student’s t-distribution, and Generalized Error Distribution (GED). To evaluate the performance of each model, the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) are used as the model selection criteria, along with forecast accuracy measures such as absolute error, root mean squared error (RMSE), and mean absolute error (MAE). Post-estimation tests, including the Autoregressive Conditional Heteroskedasticity Lagrange Multiplier (ARCH-LM) test and the Ljung-Box test, are conducted to ensure the adequacy of all models. The results reveal that all GARCH models are suitable for modeling the data, as indicated by statistically significant estimated parameters and satisfactory post-estimation outcomes. However, the EGARCH (1, 1) model, particularly with Student’s t-distribution, outperforms other models in both data fitting and accurate forecasting of nonlinear time series.

Forecasting project success in the construction industry using adaptive neuro-fuzzy inference system

Project managers often find it a challenge to successfully manage construction projects. As a result, understanding, evaluating, and achieving project success are critical for sponsors to control projects. In practice, determining key success factors and criteria to assess the performance of construction projects and forecast the success of new projects is difficult. To address these concerns, our objective is to go beyond the efficiency-oriented project success criteria by considering both efficiency- and effectiveness-oriented measures to evaluate project success. This paper contributes to existing knowledge by identifying a holistic and multidimensional set of project success factors and criteria using a two-round Delphi technique. We developed a decision support system using the Adaptive Neuro-Fuzzy Inference System (ANFIS) to forecast the success of mid- and large-sized construction projects. We gathered data from 142 project managers in Australia and New Zealand to implement the developed ANFIS. We then validated the constructed ANFIS using the K-fold cross-validation procedure and a real case study of a large construction project in Western Australia. The forecasting accuracy measures R2=0.97461, MAPE = 2.57912%, MAE = 1.88425, RMSE = 2.3610, RRMSE = 0.03149, and PI = 0.01589 suggest that the developed ANFIS is a very good predictor of project success.

Posterior Variance Test: Ex ante Evaluation of Grey Forecasting model

Scholars frequently use ex post evaluation metrics such as the Mean Absolute Percentage Error (MAPE) to estimate the forecast accuracy. However, ex ante metrics are essential to know whether a given forecasting model is suitable for a given variable irrespective of the outcome of ex post evaluations. The ex ante measures help us ensure that the forecast is accurate, not by chance. The current study presents the Posterior Variance Test (PVT), which can serve as an ex ante measure of grey forecast accuracy. The study forecasted the methane emissions from Australia and India using a grey forecasting model and found that even though the MAPE generated "accurate forecasts" for both cases, the PVT invalidated the model's suitability for one of the two cases. The data visualization also corroborated the outcome of the PVT.

A conditional decomposition of proper scores: quantifying the sources of information in a forecast

AbstractScoring rules condense all information regarding the performance of a probabilistic forecast into a single numerical value, providing a convenient framework with which to rank and compare competing prediction schemes objectively. Although scoring rules provide only a single measure of forecast accuracy, the expected score can be decomposed into components that each assess a distinct aspect of the forecast, such as its calibration or information content. Since these components could depend on several factors, it is useful to evaluate forecast performance under different circumstances; if a forecaster were able to identify situations in which their forecasts perform particularly poorly, then they could more easily develop their forecast strategy to account for these deficiencies. To help forecasters identify such situations, a novel decomposition of scores is introduced that quantifies conditional forecast biases, allowing for a more detailed examination of the sources of information in the forecast. From this, we claim that decompositions of proper scores provide a broad generalisation of the well‐known analysis of variance (ANOVA) framework. The new decomposition is applied to the Brier score, which is then used to evaluate forecasts that the daily maximum temperature will exceed a range of thresholds, issued by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss). We demonstrate how the additional information provided by this decomposition can be used to improve the performance of these forecasts, by identifying appropriate auxiliary information to include within statistical postprocessing methods.

Naïve Forecasting: A Tool to Compare Forecast Models

In this study, a freshly fitted forecast model is put up against a standard procedure for comparison. But first, the essay makes a distinction between the confusing notion of a prediction model's accuracy measure and a comparison of forecast models in terms of gauging their relative and absolute accuracy measures in various scenarios. A forecast model's accuracy measure by itself does not give a complete picture of how much better a newly fitted model is than other benchmark models built from the same dataset. This article illustrates the comparison of a multiple regression model as a novel fit with the naive forecasting methodology, a well-known benchmark in the forecasting area, using cross-validation techniques. The performance of the forecast models was assessed using two generally used accuracy measures, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). It was discovered that the multiple regression model performs better than the naive technique in both MAE and MAPE. This meant the multiple regression model was a worthy fit. In summary, it is crucial to compare a newly developed forecast model with benchmark models to evaluate its performance accurately. This process allows for the identification of the most suitable forecasting method for a specific context and promotes the development of improved techniques for comparing forecast models in the future.

Forecasting container throughput of major Asian ports using the Prophet and hybrid time series models

Forecasting container throughput is critical for improved port planning, operations, and investment strategies. Reliability of forecasting methods need to be ensured before utilizing their outcomes in decision making. This study compares forecasting performances of various time series methods, namely autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), Holt-Winter's Exponential Smoothing (HWES), and the Prophet model. Since forecast combinations can improve performance, simple and weighted combinations of ARIMA, SARIMA and HWES have been explored, too. Monthly container throughput data of port of Shanghai, Busan, and Nagoya are used. The Prophet model outperforms others in the in-sample forecasting, while combined models outperform others in the out-sample forecasting. Due to the observed differences between the in-sample and out-sample forecast accuracy measures, this study proposes a forecast performance metric consistency check approach for informed real-world applications of forecasting models in port management decision-making.

Price Prediction for Bitcoin: Does Periodicity Matter?

Purpose: A major challenge traders, speculators and investors are grappling with is how to accurately forecast Bitcoin price in the cryptocurrency market. This study is aimed to uncover the best model for the forecasts of Bitcoin price as well as to verify the price series that offers the best predictions performance under different periodicity of datasets. Design/methodology/approach: The study adopts three different data periods to verify whether frequency matters in forecasting Bitcoin price. The Bitcoin price, from 01/01/15 to 11/01/2021, is trained and validated on selected forecast models, including the Naïve, Linear, Exponential Smoothing Model, ARIMA, Neural Network, STL and Holt-Winters filters. Five forecast accuracy measures (RSME, MAE, MPE, MAPE and MASE) are applied to confirm the best performing model. The Diebold‐Mariano test is used to compare the forecasts based on the daily price with those based on the weekly and monthly. Findings: Based on the accuracy measures, the results indicate that the Naïve model provides more accurate performance for the daily series, while the linear model outperforms others for the weekly and monthly series. Using the Diebold‐Mariano statistics, there is evidence that forecasting Bitcoin price is not sensitive to the data periodicity. Research limitations/implications: The study has a major limitation, which is the shared sentiment to apply actual Bitcoin price series, and not the returns or log transformation for the forecast models. Notably, actual data may sometimes be loud, hence increasing the possibility of over predictions. Originality/value: In forecasting, different approaches have been used, this paper compares outputs of both statistical and machine learning methods in order to arrive at the best option for the Bitcoin price forecasts. Hence, we investigate whether the machine learning tools offer better forecasts in terms of lower error and higher model’s accuracy relative to the traditional models.

Application and performance of data mining techniques in stock market: A review

SummaryPrediction and the stock market go hand in hand. Due to the inherent limitations of traditional forecasting methods and the pursuit to uncover the hidden patterns in stock market data, stock market prediction using data mining techniques has caught the fancy of academicians, researchers, and investors. Based on a systematic review of more than 143 research studies spanning 25 years, the present paper brings to light the major issues concerning forecasting of stock markets based on data mining techniques, such as usage of data mining techniques in the stock market, input data types, single versus hybrid techniques, instruments and stock markets researched, types of software and algorithms used, measures of forecast accuracy, and performance of various data mining techniques. Emerging patterns related to various dimensions have been critically analyzed by highlighting the existing limitations and suggesting future research paradigms. This analysis can be useful for academicians, researchers and investors looking for futuristic directions in a given research domain.

Short-term Water Level Forecast Using ANN Hybrid Gaussian-Nonlinear Autoregressive Neural Network

The aim of this study is to develop the best forecast model using hybrid Gaussian-Nonlinear Autoregressive Neural Network to forecast the water level with multiple hoursahead for Melaka River. The developmentof flood forecast models is crucial and has led to risk control, policy recommendations, a reduction in human life loss, and a reduction in flood-related property destruction. In this research, Artificial Neural Network (ANN) approach was used to forecast flood by modeling and forecasting water level time series.ANN approach was selected due to its high reputation abilities to learn from the time-series data pattern. A total of 2782data for the period of one month wasused in ANN training, validation, and testing to forecast the flash flood. In this study,Hybrid Gaussian Nonlinear Autoregressive Neural Network (Gaussian-NAR) was used as the ANN approach to forecasting the water level time series. This study's primary focus is to find the most appropriate forecast model to forecast the water level in multiple time steps ahead, which are 1 hour, 3 hours, 5 hours, and 7 hours. The forecast accuracy measures are measured using the Pearson R and R-squared to find the most accurate model for this multiple time-step ahead. The result indicates that with 7 hours forecast ahead, the R squared is 86.7%. The best model in the Gaussian-NAR forecast is a 3-hour water level forecast with the R squared of 99.8 percent and had the best model performance result.

Forecasting annual electric power consumption using a random parameters model with heterogeneity in means and variances

This paper develops heterogeneity-based econometric models that forecast the yearly consumption of electricity. Unlike previous research, this paper departs from classical econometric regression models and explicitly accounts for unobserved heterogeneity and corresponding interactions. More specifically, three modeling approaches are applied: a random parameter linear regression model (RPLRM), a correlated random parameter linear model (C-RPLRM), and a random parameter linear model with heterogeneity in means and variances (RP-HMV). In addition, a grey model is estimated based on historical consumption data. The estimation results clearly demonstrate that the random parameter methodology is statistically superior to the classical multiple linear regression model. Moreover, based on the test results reported in this paper, as well as the forecasting accuracy measures, the RP-HMV model is shown to be statistically outstanding, with a very low forecasting error (MAPE of 0.04%) compared to the other models, including the grey model. Our estimation results show that two variables, namely the average electricity price and contribution of renewable energy to the national and distribution grids, produced statistically significant random parameters with heterogeneous variances and means. Moreover, the results show that factors including the number of households on the distribution grid, average electric power price, and availability of all-season air conditioners with hot and cold inverter characteristics significantly influenced the electricity consumption. These empirical results reveal that random-parameters models with heterogeneity in their means and variances can provide a detailed analysis of the predictor variables shaping the annual electricity consumption, and they highlight the importance of including unobserved heterogeneity and related interactions in econometric models.