Forecasters Research Articles

PREDIKSI PENJUALAN MELALUI METODE WEIGHTED MOVING AVERAGE (WMA) PADA UMKM “Nasi Bakar”

This study aims to find out the results of sales forecasting calculations using the Weight Moving Average method in Nurochmah Nasi Bakar MSMEs from April 2023 to July 2024. In addition, to determine the accuracy of forecast errors in sales forecasting calculations and the application of the Weighted Moving Average (WMA) method in predicting sales to Nurochmah MSMEs. The research method used is descriptive quantitative using historical sales data from January 2023 to July 2024, to predict sales using the WMA method at Nasi Bakar Nurochmah MSMEs from April 2023 to July 2024 using Production and Operations Management-Quality Control (POM-QM) software. The results of the calculation of sales forecasting using the WMA method on Nurochmah grilled rice MSMEs for April 2023 to July 2024 fluctuated as follows: December was 768.4 packs, a decrease of 4.547% from the actual 805 packs. January was 772.7 packs, a decrease of 0.553% from the actual 777 packs, February was 781.4 packs, an increase of 5.31% from the actual 742 packs. The accuracy of forecast error in calculating sales forecasting using the WMA method from April 2023 to July 2024 obtained an accuracy value of < 10% measured using the Mean Absolute Percent Error (MAPE). The average MAPE is 1.983%, it can be said that the forecasting ability of this method is very accurate. This shows that the WMA method can be applied to Nurochmah grilled rice MSMEs, so that it can help control sales volume, as well as as a basis for decision-making and planning sales targets for the next period.

Kausalitas Antara Variabel Makro dan Stunting di Sumatera Barat Periode 2021 -2023

This research intends to look at the causal relationship between stunting, unemployment, poverty, income inequality, human development index in West Sumatra for the period 2021 - 2023. The type of data used is secondary data in the form of panel data from the Central Statistics Agency (BPS) and the Indonesian Nutrition Status Survey from 2021 to 2023 mapping of 19 regencies/cities in West Sumatra. The data analysis used is vector auto regression analysis (VAR) and vector error correction model (VECM). In inductive analysis there are several tests, namely Optimum Lag Test, Model Stability Test, Johansen Cointegration Test, Granger Causality Test (Granger Causality), VECM Estimation, Impulse Response Function (IRF) Analysis, Forecast Error Variance Decomposition (FEVD) The results of this study show that there is a significant relationship between the human development index and income inequality. There is a significant relationship between unemployment and poverty and income inequality significant relationship between stunting and HDI, poverty, income inequality and unemployment.

The Forecasting of the Spread of Infectious Diseases Based on Conditional Generative Adversarial Networks

New epidemics encourage the development of new mathematical models of the spread and forecasting of infectious diseases. Statistical epidemiology data are characterized by incomplete and inexact time series, which leads to an unstable and non-unique forecasting of infectious diseases. In this paper, a model of a conditional generative adversarial neural network (CGAN) for modeling and forecasting COVID-19 in St. Petersburg is constructed. It takes 20 processed historical statistics as a condition and is based on the solution of the minimax problem. The CGAN builds a short-term forecast of the number of newly diagnosed COVID-19 cases in the region for 5 days ahead. The CGAN approach allows modeling the distribution of statistical data, which allows obtaining the required amount of training data from the resulting distribution. When comparing the forecasting results with the classical differential SEIR-HCD model and a recurrent neural network with the same input parameters, it was shown that the forecast errors of all three models are in the same range. It is shown that the prediction error of the bagging model based on three models is lower than the results of each model separately.

Determinants of the Developed Country Index and Indonesian Macroeconomic on the IDX Growth 30 and IDX Value 30

This study analyzes the determinants of the Developed Country Index (Hang Seng Index, Dow Jones Index) and Indonesian macroeconomic variables (10-Year Government Bond Yield, Foreign Ownership in Government Bonds, and the BI-7 Day Reverse Repo Rate, BI7DRR) on the movement of the IDX Growth 30 Index (IDXG30) and IDX Value 30 Index (IDXV30) from 2018–2022. Monthly time series data is used with a saturated sampling technique, processed via Eviews 12 using VECM analysis. The study conducts Stationarity, Optimal Lag, VAR Stability, and Cointegration Tests, along with Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analyses Results show that the Hang Seng Index and Foreign Ownership in Government Bonds negatively affect IDXG30 and IDXV30, while BI7DRR positively influences both. The Dow Jones Index positively affects IDXG30 but negatively impacts IDXV30. Meanwhile, the 10-Year Government Bond Yield negatively affects IDXG30 and positively influences IDXV30. Additionally, the performance of IDXG30 and IDXV30 themselves and BI7DRR significantly contribute to both indices' movements.

Global atmospheric state analysis using objective Gaussian probability density functions

Atmospheric state analysis is a difficult scientific problem due to the chaotic nature of the atmosphere. Data assimilation is a framework for generating an accurate state analysis of a physical system using probability density functions (PDFs) describing uncertainty of information on the state of the physical system. However, since PDFs cannot be deduced theoretically, those used in data assimilation of atmospheric state analysis are based on empirical tunings. This PDF uncertainty limits the theoretical consistency and accuracy of atmospheric state analysis and that of all atmospheric sciences. In this study, we constructed a highly accurate and theoretically consistent atmospheric state analysis by objectively estimating the PDFs of all datasets (forecasts and observations) under the Gaussian approximation. We show that an ensemble of data assimilations with 192 members using the four-dimensional variational method and sample statistics obtained with the data assimilation theory (Desroziers’ method) can generate more accurate objective Gaussian PDFs, including flow-dependent forecast error structures. Numerical experiments of atmospheric state analysis and forecasts using objective PDFs were conducted and compared with those using conventional empirical PDFs. The objective PDFs had smaller error variances for most data (about 34% of those of CNTL on average) and larger observation error correlations for satellite radiances, where the strongest correlation was greater than 0.8. The analysed atmospheric states are systematically different, such as a cooler (exceeding 1.2 K) and wetter (exceeding 1.2 g/kg) low troposphere in regions characterized by low-level clouds off the west coast of the continents. The theoretical consistency evaluated by the chi-square-based tests showed a clear improvement from 16 to 95%. The forecast accuracy was improved globally up to 9%, with 95% statistical significance. The tropical cyclone track forecast accuracy was also improved about 20%.

Predictive ARIMA Model with a Machine Learning (ML) Approach for COVID-19 Data in Pakistan

This study is based on the application of an ARIMA (p, d, q) based machine learning (ML) approach to evaluate the dynamics of COVID-19 pandemic. The focus is on estimating epidemic trends and performing diagnostic scrutiny with model fitting. The data including all four waves of the pandemic pertaining to Pakistan, covering all four provinces (Sindh, Punjab, Khyber Pakhtunkhwa, Balochistan, as well as Gilgit Baltistan, Azad Jammu Kashmir, and the capital city Islamabad, collected from February 26, 2020, to September 30, 2021, is analyzed. The ML algorithm is used to optimize the results of ADF, unit root test which ensures the minimum of ACF, and PACF graphs intention of the data series. The results employ the fitted ARIMA models (1, 1, 1) and (1, 1, 7) for the 1st to 4th waves, confirming daily infected cases across the entire dataset of Pakistan. The cumulative trained observations are from the 1st wave (February 26, 2020, to October 20, 2020), 2nd wave (October 21, 2020, to March 16, 2021), 3rd wave (March 17, 2021, to July 10, 2021), and 4th wave (July 11, 2021, to September 30, 2021), with a further 14-day forecast (from October 1 to October 14, 2021). The results show a strong correlation between the trained and predicted values, ranging from 0.8789 to 0.99236. To select predictive model parameters, the model that results in the minimum Bayesian Information Criterion (BIC) value and residuals from the datasets obtained after detaching the unnecessary errors and the 95^% CI for the forecasting error ( ) are calculated. These values would help to decide the best fitted predictive model.

Cooperative energy interaction for neighboring multiple distribution substation areas considering demand response

With the growing integration of renewable energy into medium- and low-voltage distribution networks, the distribution substation area (DSA) has emerged, encompassing energy storage and loads. This paper introduces an energy interaction framework for multiple DSAs aimed at enhancing local renewable energy consumption. The energy interaction issue among various DSAs is modeled as a Nash bargaining problem to encourage energy exchanges. However, the variability in pricing and internal demand response may influence scheduling decisions, necessitating further investigation. To address price forecast errors, scenarios are developed using a stochastic programming approach to represent price uncertainties while adjusting the DSA’s load accordingly. Optimal power flow constraints are integrated into the model to bolster power system operation security. Additionally, the transmission capacity can impact scheduling outcomes and operational costs. The influence of transmission limitations on operational strategies is examined within the allowable capacity. To solve this issue, the bargaining model is divided into two subproblems, and an enhanced alternating direction multiplier method (ADMM) is used to maintain the privacy of DSAs. The simulation results obtained using the IEEE-33 bus system indicate that energy interaction among multiple DSAs significantly lowers operating costs and facilitates the integration of renewable energy.

How Important are Commodity Terms of Trade Shocks in Explaining Government Tax Revenue Fluctuations? Evidence from Ethiopia

Using a new database of commodity terms of trade and identifying country-specific commodity export and import price shocks separately, this article mainly aims to examine the importance of commodity export and import price shocks in explaining government tax revenue fluctuations using Ethiopia as an example over the sample period 1980–2019. This article employs a structural vector autoregressive model and forecast error variance decomposition analysis to address this objective. The results show that commodity export and import price shocks account for around 48% of government tax revenue fluctuations. While commodity export price shocks contribute to around 37% of fluctuations in government tax revenue, commodity import price shocks account for only 11% of government tax revenue fluctuations. These findings are discussed in terms of their policy implications. JEL Classification C32, F41, F44

The Ensemble Tangent Linear Model Applied to a Cloud Physics Parameterization

Abstract An Ensemble Tangent Linear Model (ETLM) is applied to a cloud physics scheme used in the Navy Global Environmental Model (NAVGEM). The ensemble is created using 3-hour forecasts from the Ensemble Transform method used in the NAVGEM data assimilation system. The model states are saved before and after applying the cloud physics parameterization (which includes condensation/evaporation of cloud ice and cloud liquid water and stratiform precipitation), and these states are used to construct linearized model tendencies for temperature, specific humidity, cloud liquid water, and cloud ice water. We examine separately the application of the ETLM to cloud physics components that are explicitly local versus non-local. For the local components, an ETLM is built using a single grid point. ETLMs from 50 to 1000 members are tested, and skillful forecasts can be obtained for both local and non-local physics even with a moderate sized ensemble (e.g., 100 members). At 1000 members, the globally-averaged forecast error reductions (relative to persistence errors) are ∼40% for temperature, water vapor, and cloud liquid water and ∼30% for cloud ice. When initial perturbations are reduced by a factor of 0.1, the error reductions increase to ∼65% for all variables. For physics with non-local components (stratiform precipitation) the covariances that comprise the ETLM are localized with a Schur product matrix using a Gaussian localization shape with tunable length. The optimal lengths increased with ensemble size from ∼2-3 km for 50 members to ∼10 km for 1000 members. ETLMs for “all cloud physics” are also constructed and evaluated.

A natural gas consumption forecasting system for continual learning scenarios based on Hoeffding trees with change point detection mechanism

Forecasting natural gas consumption, considering seasonality and trends, is crucial in planning its supply and consumption and optimizing the cost of obtaining it, mainly by industrial entities. However, in times of threats to its supply, it is also a critical element that guarantees the supply of this raw material to meet individual consumers’ needs, ensuring society’s energy security. This article introduces a novel multistep forecasting of natural gas consumption with change point detection integration for model collection selection with continual learning capabilities using data stream processing. The performance of the forecasting models based on the proposed approach is evaluated in a complex real-world use case of natural gas consumption forecasting. Furthermore, the methodology generability was verified in an electricity load forecasting task. We employed Hoeffding tree predictors as forecasting models and the Pruned Exact Linear Time (PELT) algorithm for the change point detection procedure. The change point detection integration enables the selection of a different model collection for successive time frames. Thus, three model collection selection procedures are defined and evaluated for forecasting scenarios with various densities of detected change points. These models were compared with change point agnostic baseline approaches and deep learning models. Our experiments show that the proposed approach provides superior results to deep learning models for both datasets and that fewer change points result in a lower forecasting error regardless of the model collection selection procedure employed.

Calibration of Typhoon Track Forecasts Based on Deep Learning Methods

An accurate forecast of typhoon tracks is crucial for disaster warning and mitigation. However, existing numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, still exhibit significant errors in track forecasts. This study aims to improve forecast accuracy by correcting WRF-forecasted tracks using deep learning models, including Bidirectional Long Short-Term Memory (BiLSTM) + Convolutional Long Short-Term Memory (ConvLSTM) + Wide and Deep Learning (WDL), BiLSTM + Convolutional Gated Recurrent Unit (ConvGRU) + WDL, and BiLSTM + ConvLSTM + Extreme Deep Factorization Machine (xDeepFM), with a comparison to the Kalman Filter. The results demonstrate that the BiLSTM + ConvLSTM + WDL model reduces the 72 h track prediction error (TPE) from 255.18 km to 159.23 km, representing a 37.6% improvement over the original WRF model, and exhibits significant advantages across all evaluation metrics, particularly in key indicators such as Bias2, Mean Squared Error (MSE), and Sequence. The decomposition of MSE further validates the importance of the BiLSTM, ConvLSTM, WDL, and Temporal Normalization (TN) layers in enhancing the model’s spatio-temporal feature-capturing ability.

Multistage stochastic programming for integrated network optimization in hurricane relief logistics and evacuation planning

AbstractIn this article, we study the integrated hurricane relief logistics and evacuation planning (IHRLEP) problem, integrating hurricane evacuation and relief item pre‐positioning operations that are typically treated separately. We propose a fully adaptive multistage stochastic programming (MSSP) model and solution approaches based on two‐stage stochastic programming (2SSP). Utilizing historical forecast errors modeled using the auto‐regressive model of order one, we generate hurricane scenarios and approximate the hurricane process as a Markov chain, and each Markovian state is characterized by the hurricane's location and intensity attributes. We conduct a comprehensive numerical experiment based on case studies motivated by Hurricane Florence and Hurricane Ian. Through the computational results, we demonstrate the value of fully adaptive policies given by the MSSP model over static ones given by the 2SSP model in terms of the out‐of‐sample performance. By conducting an extensive sensitivity analysis, we offer insights into how the value of fully adaptive policies varies in comparison to static ones with key problem parameters.

A predictive fuzzy logic and rule-based control approach for practical real-time operation of urban stormwater storage system

Predictive real-time control (RTC) strategies are usually more effective than reactive strategies for the intelligent management of urban stormwater storage systems. However, it remains a challenge to ensure the practicality of RTC strategies that use accessible, non-idealized predictive information while improving their efficiency for successive rainfall events instead of specific phases. This study developed a predictive fuzzy logic and rule-based control (PFL-RBC) approach to address the continuous control of individual storage systems. This approach incorporates total rainfall depth forecast information with an intra-storm fuzzy logic system to optimize peak flow control and several rule-based strategies for pre-storm water detention, reuse, and release control. Computational experiments were conducted using a storage tank case study to test the proposed approach under various rainfall conditions and storage sizes. The results showed that PFL-RBC outperformed static rule-based control in infrequent design storms and realistic continuous rainfall events, reducing flood peaks and volumes by 55 %∼87 % and 7 %∼20 %, respectively, and significantly increasing water detention time and reuse volume. Meanwhile, PFL-RBC required less predictive information to achieve a 6 %∼15 % advantage in peak flow control compared to optimized model predictive control. More importantly, PFL-RBC was reliable in the face of input uncertainty, with <25 % performance loss for water quantity control when the realistic forecast error ranged from -50 % to +50 %. These findings suggest that the proposed approach has great potential to enhance the efficiency and practicality of stormwater storage operations.

Public debt determinants: A time-varying analysis of core and peripheral Euro area countries

This study employs a Bayesian Interacted Panel VAR model to estimate time-varying Generalized Forecast Error Variance Decomposition, analyzing how key determinants affect debt in Core and Peripheral Euro Area countries. Results highlight varying effects of determinants across periods and subgroups.

Time‐series‐based ensemble model output statistics for temperature forecasts postprocessing

AbstractThe uncertainty in numerical weather prediction models is nowadays quantified by the use of ensemble forecasts. Although these forecasts are continuously improved, they still suffer from systematic bias and dispersion errors. Statistical postprocessing methods, such as the ensemble model output statistics (EMOS), have been shown to substantially correct the forecasts. This work proposes an extension of EMOS in a time‐series framework. Besides taking account of seasonality and trend in the location and scale parameter of the predictive distribution, the autoregressive process in the mean forecast errors or the standardized forecast errors is considered. The models can be further extended by allowing generalized autoregressive conditional heteroscedasticity. Furthermore, it is outlined how to use these models for arbitrary forecast horizons. To illustrate the performance of the suggested EMOS models in time‐series fashion, we present a case study for the postprocessing of 2 m surface temperature forecasts using five different lead times and a set of observation stations in Germany. The results indicate that the time‐series EMOS extensions are able to significantly outperform the benchmark models EMOS and autoregressive EMOS (AR‐EMOS) in most of the lead time–station cases.

Research on the risk spillover effect of carbon emission trading market and various industry markets in China and the European Union

AbstractWidely concerns over the carbon emission problems have been aroused. Prior studies have documented the correlation between the carbon emission market and industries separately. This study compared the tail risks and risk spillover effect of the carbon emission and 11 industries markets in China and the European Union (EU) by using the multivariate multi‐quartile conditional autoregressive at‐risk model. Moreover, to evaluate the risk spillover of each market under extreme risk conditions in time domain and frequency domain, DY spillover index and BK spillover index were constructed via generalized forecast error variance decomposition and generalized causation spectrum, respectively. Findings are as follows: (1) The tail risks and fluctuation of the trend of Chinese industry markets reflects more higher and larger than those in the EU; (2) The EU suffers from smaller external shocks, while China has the opposite result and can recover relatively faster; (3) In China, energy, industrial, information technology, financial, real estate, consumer goods, carbon emissions, and discretionary consumption industries are risk spillover industries, while healthcare, materials, telecommunication services, and utilities industries are risk receiving industries. In contrast, the risk spillover industries in the EU remain consistent with those of the Chinese markets except for the materials, discretionary consumption, consumer goods, information technology and real estate industries. (4) On the short‐term, medium‐term and long‐term scales, the risk spillover of China's carbon emission trading market and various industries is basically consistent with that of the EU. These findings contribute to reducing greenhouse gas emissions and achieving the goal of carbon peak and carbon neutrality.

Study on Downscaling Correction of Near-Surface Wind Speed Grid Forecasts in Complex Terrain

Accurate forecasting of wind speeds is a crucial aspect of providing fine-scale professional meteorological services (such as wind energy generation and transportation operations etc.). This article utilizes CMA-MESO model forecast data and CARAS-SUR_1 km ground truth grid data from January, April, July, and October 2022, employing the random forest algorithm to establish and evaluate a downscaling correction model for near-surface 1 km resolution wind-speed grid forecast in the complex terrain area of northwestern Hebei Province. The results indicate that after downscaling correction, the spatial distribution of grid forecast wind speeds in the entire complex terrain study area becomes more refined, with spatial resolution improving from 3 km to 1 km, reflecting fine-scale terrain effects. The accuracy of the corrected wind speed forecast significantly improves compared to the original model, with forecast errors showing stability in both time and space. The mean bias decreases from 2.25 m/s to 0.02 m/s, and the root mean square error (RMSE) decreases from 3.26 m/s to 0.52 m/s. Forecast errors caused by complex terrain, forecast lead time, and seasonal factors are significantly reduced. In terms of wind speed categories, the correction significantly improves forecasts for wind speeds below 8 m/s, with RMSE decreasing from 2.02 m/s to 0.59 m/s. For wind speeds above 8 m/s, there is also a good correction effect, with RMSE decreasing from 2.20 m/s to 1.65 m/s. Selecting the analysis of the Zhangjiakou strong wind process on 26 April 2022, it was found that the downscaled corrected forecast wind speed is very close to the observed wind speed at the station and the ground truth grid points. The correction effect is particularly significant in areas affected by strong winds, such as the Bashang Plateau and valleys, which has significant reference value.

Credit Cycles, Expectations, and Corporate Investment

Abstract We provide a systematic empirical assessment of the Minsky hypothesis that business fluctuations stem from irrational swings in expectations. Using predictable firm-level forecast errors, we build an aggregate index of irrational expectations and use it to provide three sets of results. First, we show that our index predicts aggregate credit cycles. Next, we show that these predictable credit cycles drive cycles in firm-level debt issuance and investment and similar cycles between financially constrained and unconstrained firms, as Minsky predicts. Finally, we show more pronounced cycles in firm-level financing and investment for firms with ex ante more optimistic expectations. (JEL G31, G32, G40, E32, E44)

Evaluating the influence of Nano-GO concrete pavement mechanical properties on road performance and traffic safety using ANN-GA and PSO techniques

The burgeoning demand for durable and eco-friendly road infrastructure necessitates the exploration of innovative materials and methodologies. This study investigates the potential of Graphene Oxide (GO), a nano-material known for its exceptional dispersibility and mechanical reinforcement capabilities, to enhance the sustainability and durability of concrete pavements. Leveraging the synergy between advanced artificial intelligence techniques—Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO)—it is aimed to delve into the intricate effects of Nano-GO on concrete's mechanical properties. The empirical analysis, underpinned by a comparative evaluation of ANN-GA and ANN-PSO models, reveals that the ANN-GA model excels with a minimal forecast error of 2.73%, underscoring its efficacy in capturing the nuanced interactions between GO and cementitious materials. An optimal concentration is identified through meticulous experimentation across varied Nano-GO dosages that amplify concrete's compressive, flexural, and tensile strengths without compromising workability. This optimal dosage enhances the initial strength significantly, and positions GO as a cornerstone for next-generation premium-grade pavement concretes. The findings advocate for the further exploration and eventual integration of GO in road construction projects, aiming to bolster ecological sustainability and propel the adoption of a circular economy in infrastructure development.

Traffic Prediction as a Multidimensional Random Process in a Three-Dimensional High-Density Internet of Things Network

Relevance. The relevance of the topic considered in the article lies in the active transformation of communication networks and the formation of a three-dimensional high-density communication network, which changes the structure of data traffic, therefore, for this type of network, a traffic model is considered as a multidimensional random process. The main purpose of the study is to improve the efficiency of network traffic forecasting by developing a method, the distinctive feature of which is traffic forecasting as a multidimensional random process, taking into account the mutual dependence of individual flows produced by network nodes. Methods. The paper considers an algorithm for training an artificial neural network (ANN) based on the method of reducing the root of the mean square error RMSE, and also proposes forecasting methods using LSTM-type ANNs and adapting model parameters to changing network operating conditions. The use of LSTM-type ANN for forecasting a multivariate random process describing traffic in a three-dimensional high-density network can yield better results than forecasting individual traffic flows as independent random processes due to the consideration of mutual influences between different traffic flows.The results. Building the corresponding model, collecting statistics (obtaining a training sample), training the ANN and performing the forecast require the use of computing resources. Thus, the forecasting efficiency can be defined as a decrease in the forecasting error while maintaining the volume of resources used or a decrease in the volume of resources while maintaining the forecasting error. In the course of solving the scientific problem, criteria were identified for selecting the value of a unit interval (lag), which, together with the forecasting interval, significantly affects the final scenario.The theoretical significance The scientific novelty of the work lies in the assessment of the change in the error in forecasting the traffic of a three-dimensional high-density communication network as a multivariate random process, compared to presenting the forecast of the same traffic as a set of independent random processes.Significance (theoretical). The efficiency of traffic forecasting as a multidimensional random process in a three-dimensional high-density communication network increases with increasing dimensionality. Thus, such traffic in forecasting problems should be considered as a multidimensional random process, the dimensionality of which is equal to the number of network nodes producing traffic.Significance (practical). The results obtained in the work can be used in the future to optimize the functioning of the traffic management system.