Forecast Accuracy Research Articles

Electric vehicle charging station demand prediction model deploying data slotting

Accurate prediction of energy requirement at charging station is essential for optimizing infrastructure usage, ensuring grid stability, and minimizing operational cost. Literatures suggest deployment of machine learning techniques to forecast the station demand. One major challenge associated with development of machine learning models is the inherent uncertainty in electric vehicle charging behaviour that includes variations in charging patterns, user preferences, and vehicle types. The conventional pre-processing techniques fail to dislodge nonlinearities and highly random patterns that include very low or zero-charging. Employing such techniques affects the model's forecast accuracy. This article performs data-slotting during pre-processing stage and then selects the best among 1-h, 2-h, 3-h and 4-h slots, to frame the feature vectors. The 4-h data with minimum variance is suggested to frame the dataset. Four distinct datasets, comprising different combination of average and total demands as predictor and response respectively are considered. The created dataset is deployed in Random Forest, Categorical Boosting, Extreme Gradient Boosting and Light Gradient Boosting models. The article recommends Categorical Boosting Regression model with least mean absolute error, mean square error and root mean square error of 0.0726, 0.0112, and 0.1059 respectively. Furthermore, the use of feature vector comprising of aggregated load for prescribed slots and the response representing the aggregated demand is observed to provide the least prediction error by the suggested model. The suggested model fed by the proposed feature vector offers significant advantage to charging station operator by enhancing the operational efficiency while performing resource and cost management with strategic planning.

Advanced Analytics for Retail Inventory and Demand Forecasting

Achieving operational efficiency and enhancing customer satisfaction levels in the retail sector is directly dependent on efficient inventory management and accurate demand forecasting. The following study employs advanced analytics techniques, such as time series forecasting and machine learning, to bolster these essential functions. By leveraging on historical sales data from 45 retail stores sourced by Kaggle, this paper has constructed predictive models with the aim to optimize inventory levels and forecast demand with precision. The Seasonal AutoRegressive Integrated Moving Average with Exogenous Regressors (SARIMAX) model employed in this study adequately captures linear dependencies and seasonal patterns, while Long Short-Term Memory (LSTM) networks are responsible for the management of intricate, non-linear dependencies. The findings from this study depict the significant seasonal trends, the impact of economic factors, the impact of economic variables and the effectiveness of hybrid models in improving forecast accuracy. The integration of such advanced methodologies clearly highlights their massive potential in improving aspects such as inventory management and operational efficiency in the retail domain.

Forecasting Non-stationary Time Series Using Deep Learning in a Fuzzy Time Series Framework and its Application to Stock Markets

Non-stationary time series prediction is challenging due to its dynamic and complex nature. Fuzzy time series models offer a promising solution for forecasting such data, but a key challenge lies in partitioning the universe of discourse, which significantly impacts forecasting accuracy. Traditional fuzzy time series models often use equal-length interval partitioning, which is more suited for stationary data and limits their adaptability to non-stationary time series. This paper introduces a novel variable-length interval partitioning method designed specifically for non-stationary time series. The developed method combines a Long Short-Term Memory (LSTM) Autoencoder with K-means clustering, enabling dynamic, data-driven partitioning that adapts to the changing characteristics of the data. The LSTM Autoencoder encodes the time series, which is clustered using K-means, and intervals are defined based on cluster centers. Furthermore, the Variable Length Interval Partitioning-based Fuzzy Time Series model (VLIFTS) is developed by incorporating this partitioning method and the concepts of Markov chain and transition probability matrix. In this model, fuzzy sets are viewed as states of a Markov chain, and transition probabilities are used in the forecasting phase. The model is validated on stock market indices Nifty 50, NASDAQ, S&P 500, and Dow Jones. Stationarity and heteroscedasticity are tested using Augmented Dickey-Fuller (ADF) and Levene's tests respectively. Statistical forecast accuracy metrics Root Mean Squared Error (RMSE) and Mean Absolute Percent Error (MAPE) show that VLIFTS significantly improves forecasting accuracy over traditional models. This hybrid approach enhances fuzzy time series modelling and can be applied to various non-stationary time series forecasting problems.

Forecasting ETF Performance: A Comparative Study of Deep Learning Models and the Fama-French Three-Factor Model

The global financial landscape has witnessed a significant shift towards Exchange-Traded Funds (ETFs), with their market capitalization surpassing USD 10 trillion in 2023, due to advantages such as low management fees, high liquidity, and broad market exposure. As ETFs become increasingly central to investment strategies, accurately forecasting their performance has become crucial. This study addresses this need by comparing the efficacy of deep learning models against the traditional Fama-French three-factor model in predicting daily ETF returns. The methodology employs eight artificial neural network architectures, including ANN, LSTM, GRU, CNN, and their variants, implemented in Python and applied to data ranging from 2010 to 2020, while also exploring the impact of additional factors on forecast accuracy. Empirical results reveal that LSTM and the Fama-French three-factor model exhibit a superior performance in ETF return prediction. This study contributes to the literature on financial forecasting and offers practical insights into investment decision making. By leveraging advanced artificial intelligence techniques, this study aims to enhance the toolkit available for ETF performance analysis, potentially improving investment strategies in this dynamic market segment.

Implementation of storm-following nest for the next-generation Hurricane Analysis and Forecast System (HAFS)

Tropical cyclones models have long used nesting to achieve higher resolution of the inner core than was feasible for entire model domains. These high resolution nests have been shown to better capture storm structures and improve forecast accuracy. The Hurricane Analysis and Forecast System (HAFS) is the new-generation numerical model embedded within NOAA’s Unified Forecast System (UFS). The document highlights the importance of high horizontal resolution (2 km or finer) in accurately simulating the small-scale features of tropical cyclones, such as the eyewall and eye. To meet this need, HAFS was developed by NOAA leveraging a high-resolution, storm-following nest. This nest moves with the cyclone, allowing better representation of small-scale features and more accurate feedback between the cyclone’s inner core and the larger environment. This hurricane following nest capability, implemented in the Finite-Volume Cubed-Sphere (FV3) dynamical core within the UFS framework, can be run both within the regional as well as global forecast systems. A regional version of HAFS with a single moving nest went into operations in 2023. HAFS also includes the first ever moving nest implemented within a global model which is currently being used for research. In this document we provide details of the implementation of moving nests and provide some of the results from both global and regional simulations. For the first time NOAA P3 flight data was used to evaluate the inner core structure from the global run.

A bidirectional gated recurrent unit based novel stacking ensemble regressor for foretelling the global horizontal irradiance

The rapid expansion of solar power generation has led to new challenges in solar intermittency, requiring precise forecasts of Global Horizontal Irradiance (GHI). Accurate GHI predictions are crucial for integrating sustainable energy sources into traditional electrical grid management. The article proposes an innovative solution, the novel Enhanced Stack Ensemble with a Bi-directional Gated Recurrent Unit (ESE-Bi-GRU), which uses machine learning (ML) boosting regressors such as Ada Boost, Cat Boost, Extreme Gradient Boost, and Gradient Boost, and Light Gradient Boost Machine acts as a base learner and the deep learning (DL) algorithms such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for both directions are taken as a meta-learner. The predictive performance of the proposed ESE-Bi-GRU model is evaluated against individual models, showing significant reductions in mean absolute error (MAE) by 86.03 % and root mean squared error (RMSE) by 66.43 %. The model's ability to minimize prediction errors, such as MAE and RMSE holds promise for more effective planning and utilization of sporadic solar resources. By improving GHI forecast accuracy, the ESE-Bi-GRU model contributes to optimizing the integration of sustainable energy sources within the broader energy grid, fostering a more sustainable and environmentally conscious approach to energy management.

Forecasting disorientation in the tax avoidance map: Tax haven subsidiaries and analyst forecasts

In the context of economic globalization, China has actively encouraged its enterprises to “Go Global” to enhance their core competitiveness. However, in recent years, many companies have been drawn to the unique features of tax havens, establishing subsidiary in these regions to exploit regulatory and institutional arbitrage rather than focusing on genuine production and operations. This study uses panel data from Chinese listed companies from 2007 to 2022 to investigate the impact of tax haven subsidiary layout on analyst forecast accuracy and the underlying mechanisms. Our findings show that tax haven subsidiary layout significantly reduces analyst forecast accuracy. This negative effect is primarily driven by increased information asymmetry and heightened operational risks caused by tax haven subsidiary layout. Moreover, the impact is more pronounced in firms with higher controlling shareholder tunneling, lower media attention, stronger product market competition, and weaker tax enforcement. Our research contributes to a deeper understanding of the adverse effects of tax haven subsidiary layout, offering crucial insights for promoting efficient capital flow and sustainable economic development in emerging markets.

Evaluation of alternative methods for forecasting the Aboriginal and Torres Strait Islander population of Australia

Assessing future demand for a wide range of services requires good quality population forecasts. Unfortunately, many past forecasts of the Aboriginal and Torres Strait Islander (Indigenous) population of Australia have proved highly inaccurate. This is due to poor data quality, missing data, and demographers’ incomplete understanding of Indigenous population change. In addition, because Indigenous population estimates are published only every 5 years and long after the reference date, forecasts are often used as preliminary population estimates. These form the denominators of various metrics used to monitor programmes aimed at improving health and social outcomes. The aim of this paper is to present an evaluation of alternative forecasting models and forecasts of the Indigenous population of Australia’s States and Territories by age and sex. Four models, differing substantially in complexity, were evaluated: (1) the simple Hamilton–Perry method, (2) the synthetic migration cohort-component model, (3) a uniregional cohort-component model with net migration, and (4) a bi-regional cohort-component model. The population forecasting methods were evaluated against several criteria, including forecast accuracy over the 2016–21 period, input data requirements, conceptual adequacy, output detail, time required to prepare, ability to create scenarios and select alternative assumptions, and ease of implementation. The Hamilton–Perry and synthetic migration cohort-component models provided greater forecast accuracy and scored well against the evaluation criteria. In the challenging data environment for modelling Indigenous populations, simpler forecasting methods offer several practical advantages and are likely to produce more accurate forecasts than more data-intensive models.

Simulating runoff changes and evaluating under climate change using CMIP6 data and the optimal SWAT model: a case study

This study examines the influence of climate change on hydrological processes, particularly runoff, and how it affects managing water resources and ecosystem sustainability. It uses CMIP6 data to analyze changes in runoff patterns under different Shared Socioeconomic Pathways (SSP). This study also uses a Deep belief network (DBN) and a Modified Sparrow Search Optimizer (MSSO) to enhance the runoff forecasting capabilities of the SWAT model. DBN can learn complex patterns in the data and improve the accuracy of runoff forecasting. The meta-heuristic algorithm optimizes the models through iterative search processes and finds the optimal parameter configuration in the SWAT model. The Optimal SWAT Model accurately predicts runoff patterns, with high precision in capturing variability, a strong connection between projected and actual data, and minimal inaccuracy in its predictions, as indicated by an ENS score of 0.7152 and an R2 coefficient of determination of 0.8012. The outcomes of the forecasts illustrated that the runoff will decrease in the coming years, which could threaten the water source. Therefore, managers should manage water resources with awareness of these conditions.

Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model

Predicting solar power generation is a complex challenge with multiple issues, such as data quality and choice of methods, which are crucial to effectively integrate solar power into power grids and manage photovoltaic plants. This study creates a hybrid methodology to improve the accuracy of short-term power prediction forecasts using a model called Transformer Bi-LSTM (Bidirectional Long Short-Term Memory). This model, which combines elements from the transformer architecture and bidirectional LSTM (Long–Short-Term Memory), is evaluated using two strategies: the first strategy makes a direct prediction using meteorological data, while the second employs a chain of deep learning models based on transfer learning, thus simulating the traditional physical chain model. The proposed approach improves performance and allows you to incorporate physical models to refine forecasts. The results outperform existing methods on metrics such as mean absolute error, specifically by around 24%, which could positively impact power grid operation and solar adoption.

Asymmetric impact of energy prices on financial cycles based on interval time series modeling

Energy prices crucially affect financial cycles. We employ the bivariate empirical mode decomposition (BEMD) model to disaggregate the daily interval data of the volatility index (VIX) and rely on the threshold autoregressive interval model (TARI) to incorporate energy prices into the forecasting model. Furthermore, the threshold interval decomposition ensemble (TIDE) is used to forecast the VIX series with nonlinearities to improve the forecasting accuracy. Moreover, we apply the root mean square error (RMSE) and the Diebold–Mariano test (DM) to evaluate the TIDE model performance across various frequency components and the final integration results. This paper demonstrates a significant correlation between energy prices and financial cycles, along with a temporal asymmetry effect. While the impact of energy prices on improving VIX forecasting is minimal in the short term, it becomes substantial over medium and long terms. Specifically, the influence of liquefied petroleum gas (LPG) prices on the VIX is notable in both medium and long terms. Our results offer new insights and methodologies for predicting financial cycles, assisting investors in evaluating volatility-related exchange-traded products. Additionally, these findings are crucial for developing more effective policies to promote green energy development.

Robust estimation for Threshold Autoregressive Moving-Average models

Threshold autoregressive moving-average (TARMA) models extend the popular TAR model and are among the few parametric time series specifications to include a moving average in a non-linear setting. The state dependent reactions to shocks is particularly appealing in Economics and Finance. However, no theory is currently available when the data present heavy tails or anomalous observations. Here we provide the first theoretical framework for robust M-estimation for TARMA models and study its practical relevance. Under mild conditions, we show that the robust estimator for the threshold parameter is super-consistent, while the estimators for autoregressive and moving-average parameters are strongly consistent and asymptotically normal. The Monte Carlo study shows that the M-estimator is superior, in terms of both bias and variance, to the least squares estimator, which can be heavily affected by outliers. The findings suggest that robust M-estimation should be generally preferred to the least squares method. We apply our methodology to a set of commodity price time series; the robust TARMA fit presents smaller standard errors and superior forecasting accuracy. The results support the hypothesis of a two-regime non-linearity characterised by slow expansions and fast contractions.

Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba

As the global demand for sustainable energy sources continues to grow, accurate prediction of photovoltaic power generation is crucial for optimizing the utilization of solar resources and enhancing the efficiency of photovoltaic systems. To improve the accuracy of photovoltaic power forecasting, this paper proposes a novel hybrid predictive model that integrates Optimized Variational Mode Decomposition (VMD), Vision Mamba (Vim) for extracting features from sky images, and advanced mechanisms like Patch Embedding and Variate-wise Cross-Attention. Initially, the proposed model employs SAO-optimized VMD to decompose the photovoltaic power series into high, medium, and low-frequency components. Subsequently, these components are patched to serve as input for the subsequent layers. In the third step, exogenous variables, including meteorological and image data, are introduced and processed through Variate Embedding combined with cross-attention mechanisms to capture the intricate interactions between these variables. Finally, by integrating the outputs from all processing steps through normalization and feed-forward layers, the final predictive results are produced. Experimental evaluations across different seasons demonstrate significant enhancements in forecasting accuracy, with the model achieving Root Mean Square Error (RMSE) values of 0.3587 in spring, 0.4376 in summer, 0.3544 in autumn, and 0.3493 in winter. Similarly, Mean Absolute Error (MAE) and Mean Squared Error (MSE) across these seasons underscore the model's effectiveness. This model offers new technical means for photovoltaic power forecasting and provides valuable decision support for the optimization and management of photovoltaic power systems.

Revolutionizing Time Series Data Preprocessing with a Novel Cycling Layer in Self-Attention Mechanisms

This paper introduces an innovative method for enhancing time series data preprocessing by integrating a cycling layer into a self-attention mechanism. Traditional approaches often fail to capture the cyclical patterns inherent to time series data, which affects the predictive model accuracy. The proposed method aims to improve models’ ability to identify and leverage these cyclical patterns, as demonstrated using the Jena Climate dataset from the Max Planck Institute for Biogeochemistry. Empirical results show that the proposed method enhances forecast accuracy and speeds up model fitting compared to the conventional techniques. This paper contributes to the field of time series analysis by providing a more effective preprocessing approach.

Does industry co-location improve corporate information environment?

This study examines how the co-location of firms within the same industry enhances their information environments. We hypothesize that benefits arise from two effects: the industry peer effect, involving intra-industry information spillovers, and the geographic peer effect, related to shared local economic conditions with nearby non-industry peers. Our analysis evaluates how these effects, both separately and jointly, impact analysts’ forecast properties and stock price informativeness regarding future earnings. We find that the industry peer effect is negatively related to forecast dispersion and positively related to forecast accuracy, while the geographic peer effect shows no significant correlation. In addition, stock price informativeness about future earnings is positively associated with the industry peer effect but not with the geographic peer effect. We also find that greater proximity to industry or local peers enables analysts to incorporate more private (relative to public) information into their earnings forecasts, thereby enhancing the firm’s information environment. JEL CLASSIFICATION: M41, D80, G10

A Multifrequency Data Fusion Deep Learning Model for Carbon Price Prediction

ABSTRACTIn response to the global need for effective management of carbon emissions and alignment with sustainable development goals, predicting carbon trading prices accurately is critical. This study introduces a multifrequency data fusion carbon price prediction model (MFF‐CPPM), addressing the nonlinear characteristics of carbon trading prices and inconsistent feature factor frequencies. The MFF‐CPPM consists of a feature‐extraction frontend, a multifrequency data fusion transformer, and a fusion regression layer, offering a novel methodological approach in forecasting studies. The model's validity was tested in Guangdong, China's largest carbon trading pilot market. The results demonstrated that the MFF‐CPPM outperformed baseline models in terms of carbon price‐prediction accuracy and trend forecasting. Additional trials conducted in Hubei and Beijing confirmed the model's robustness and generalization capabilities, providing valuable evidence of its effectiveness and reliability across varying market contexts. This study presents a novel predictive model for carbon trading prices, with a unique capability to harness data at differing frequencies. The MFF‐CPPM not only enhances forecasting accuracy but also offers an innovative approach to effectively incorporate multifrequency information. This advancement paves the way for flexible forecasting models in any scenario where data arrive at differing frequencies.

Development and evaluation of a novel force myography (FMG) based human-machine interface for transradial prosthetics: A comparative study with electromyography (EMG) techniques

Human-Machine Interfaces (HMIs) must bridge human skills with machine control, notably in active prosthesis. Electromyography (EMG) is the most popular prosthesis actuation method. However, researchers need new HMIs for active prosthesis development. This research introduces Forcemyography (FMG) based HMI using force- sensitive resistors (FSRs) to capture myoelectric data. A particularly constructed case with two integral parts—an internal and an exterior component—is FMG. This FMG enclosure adapts effortlessly to the user's limb and shape for best performance and comfort. FMG and sEMG were compared in the experimental evaluation. Stability, forecast accuracy, hair and sweat resistance, wearability, and cost-effectiveness were examined. FMG signals outperformed sEMG signals in this comprehensive examination. FMG outperformed sEMG in stability and prediction accuracy. FMG produced outstanding results right from capture without post-processing, a unique advantage. The specialised enclosure protected FSRs from outside interference and delivered precise signals. The Forcemyography (FMG)-based HMI is a breakthrough in active prosthesis technology. Its versatility, consistent signal acquisition, and user-centric design promise improved human-machine interactions. FMG leads the way to smooth, intuitive, and efficient humanmachine interface.

An open innovative inventory management based demand forecasting approach for the steel industry

This research focuses on accurately forecasting steel wire mesh demand to ensure timely order fulfillment. Various univariate time series forecasting methods were employed, including Prophet, Support Vector Regression (SVR), 1-Dimensional Convolutional Neural Network (1D-CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). These RNN-based models were implemented to enhance the dynamics of open innovation. The study also employed the split test method to assess the impact of data size on model performance. Following a comprehensive comparative analysis, LSTM outperformed other models at 80:20 data split ratio, achieving the lowest RMSE of 51,473 and MAPE of 1.43. The robust performance of LSTM underscores its ability to expertly predict complex demand patterns. Moreover, forecast accuracy was enhanced using ensemble models, with RNN-LSTM delivering the best results (RMSE of 45,931 and MAPE of 1.20) at the same data split ratio. This accurate forecasting supports steel wire mesh companies in making informed decisions regarding inventory management. The study additionally demonstrated the integration of forecasting results into inventory strategies to improve decision-making for cost optimization.

NSEMBLE FINE TUNED MULTI LAYER PERCEPTRON FOR PREDICTIVE ANALYSIS OF WEATHER PATTERNS AND RAINFALL FORECASTING FROM SATELLITE DATA

The accurate prediction of weather patterns and rainfall forecasting is critical for various sectors, including agriculture, disaster management, and water resource planning. Traditional models often struggle to capture the complex interactions between atmospheric variables, particularly when integrating diverse types of satellite data (binary, categorical, and numerical). To address this challenge, an ensemble fine-tuned multi-layer perceptron (MLP) model is developed, combining the strengths of multiple machine learning techniques for more robust predictions. The primary problem is the difficulty in handling mixed data types while maintaining high prediction accuracy. Satellite data, including binary indicators (e.g., cloud presence), categorical features (e.g., cloud types), and numerical variables (e.g., temperature, humidity, and wind speed), provide rich information but require specialized processing for effective forecasting. The proposed method involves fine-tuning an ensemble of MLP models with backpropagation, dropout regularization, and batch normalization to reduce overfitting and enhance generalization. The ensemble integrates predictions from individual MLP models, each trained on different subsets of features (binary, categorical, numerical). This technique allows the model to leverage complementary strengths and produce more accurate rainfall forecasts. Satellite data is preprocessed and normalized before training, and categorical variables are one-hot encoded to ensure compatibility with the MLP architecture. Results from testing on historical satellite weather datasets demonstrate significant improvements in forecast accuracy. The ensemble MLP achieved an accuracy of 91.3%, with a precision of 90.7%, recall of 89.5%, and an F1-score of 90.1%. The model performed exceptionally well in identifying critical rainfall events, reducing false positives by 12% compared to traditional models.

A Shared Economy Data Prediction Model Based on Deep Learning

In the dynamic and intricate shared economy, efficient resource management and forecasting are still crucial. In this research, a new prediction model is presented that aims to improve the operational efficiency of several shared economy services. Our method creatively combines Long Short-Term Memory (LSTM) networks with Genetic Algorithms (GA) to assess user demand and optimize resource deployment. We apply this methodology especially to e-scooter sharing services, but the underlying ideas and methods can be applied to other shared economy platforms as well, like peer-to-peer lending services, car sharing, and vacation rentals. The model starts with a GA to adjust the hyperparameters of the LSTM network, making the network better suited to handle specific characteristics of common economic data. Capturing the complex temporal and spatial patterns of user behavior and demand on these platforms requires this optimization. The LSTM element then predicts changes in service demand due to its capacity to analyze sequential records. Further to being useful for analyzing sequential data and predicting destiny wishes, this predictive functionality is important for a shared economy platform to correctly manage stock, allocate assets, and predict personal wishes. We use a large dataset to check our technique, demonstrating the predictive accuracy of the model and demand and its potential to aid strategic choice-making. Compared to traditional fashions, the consequences show a large development in forecast accuracy and resource allocation efficiency. Our methodology creates a robust basis for statistics-driven insights that decorate customer happiness and decorate the long-term increase of the shared financial system. This work highlights the blended ability of GA and LSTM inside the shared economy and paves the manner for future enhancements in using modern-day gadget mastering techniques to optimize and alter various shared services. In quick, effective useful resource control and forecasting in the shared economic system is tough, however our specific forecasting model combining GA-LSTM gives a manner ahead. Our technique, as it should be, predicts fluctuations in provider demand; it was first refined the usage of GA to regulate the LSTM hyperparameters. The consequences show how correct and powerful our version is and spotlight how it can enhance customer pleasure and operational performance. This research paves the manner for future trends within the software of gadgets, gaining knowledge of methods and supports the continuing enlargement and development of shared economic system offerings.