Time Series Models Research Articles

Polynomial fuzzy information granule based [formula omitted]-triple I fuzzy reasoning algorithm and its short-term forecasting of time series

Short-term forecasting of time series is an important research topic, which involves data characteristic capture and intelligent reasoning. For this topic, the Gaussian polynomial fuzzy information granule with interpretability is granulated on data, enabling the extraction of data trend characteristic, besides, the associate characteristic within the data is captured through building fuzzy association rule. Building upon the data characteristics captured in fuzzy information granule and fuzzy association rule, an intelligent reasoning algorithm called the fuzzy information granule based α-Triple I algorithm is proposed, where the membership degree of data to granule is considered in reasoning, and next the accurate level of deviation from data to granule can be inferenced. Based on the excavated data characteristics and a rational inference, a short-term forecasting model is established. Its superiority in terms of accuracy and reliability when compared to 7 other models in real time series has been tested. Notably, the prediction of the novel model is accurate because the function of FAR is identified from FAR’s truth degree, which means the validity degree of prediction. The application of the proposed model for short-term forecasting holds a potential impact across various fields.

Scaled envelope models for multivariate time series

Vector autoregressive (VAR) models have become a popular choice for modeling multivariate time series data due to their simplicity and ease of use. Efficient estimation of VAR coefficients is an important problem. The envelope technique for VAR models is demonstrated to have the potential to yield significant gains in efficiency and accuracy by incorporating linear combinations of the response vector that are essentially immaterial to the estimation of the VAR coefficients. However, inferences based on envelope VAR (EVAR) models are not invariant or equivariant upon the rescaling of the VAR responses, limiting their application to time series data that are measured in the same or similar units. In scenarios where VAR responses are measured on different scales, the efficiency improvements promised by envelopes are not always guaranteed. To address this limitation, we introduce the scaled envelope VAR (SEVAR) model, which preserves the efficiency-boosting capabilities of standard envelope techniques while remaining invariant to scale changes. The asymptotic characteristics of the proposed estimators are established based on different error assumptions. Simulation studies and real-data analysis are conducted to demonstrate the efficiency and effectiveness of the proposed model. The numerical results corroborate our theoretical findings.

The Bayesian reservoir model of psychological regulation.

Social and behavioral scientists are increasingly interested the dynamics of the processes they study. Despite the wide array of processes studied, a fairly narrow set of models are applied to characterize dynamics within these processes. For social and behavioral research to take the next step in modeling dynamics, a wider variety of models need to be considered. The reservoir model is one model of psychological regulation that helps expand the models available (Deboeck & Bergeman, 2013). The present article implements the Bayesian reservoir model for both single time series and multilevel data. Simulation 1 compares the performance of the original version of the reservoir model fit using structural equation modeling (Deboeck & Bergeman, 2013) to the proposed Bayesian estimation approach. Simulation 2 expands this to a multilevel data scenario and compares this to the single-level version. The Bayesian estimation approach performs substantially better than the original estimation approach and produces low-bias estimates even with time series as short as 25 observations. Combining Bayesian estimation with a multilevel modeling approach allows for relatively unbiased estimation with sample sizes as small as 15 individuals and/or with time series as short as 15 observations. Finally, a substantive example is presented that applies the Bayesian reservoir model to perceived stress, examining how the model parameters relate to psychological variables commonly expected to relate to resilience. The current expansion of the reservoir model demonstrates the benefits of leveraging the combined strengths of Bayesian estimation and multilevel modeling, with new dynamic models that have been tailored to match the process of psychological regulation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Comparative analysis of decision tree, time series, and semi-distributed hydrological models for runoff prediction in snowmelt-influenced and human-activity-intensive basins: a case study of the Shiribeshi-Toshibetsu basin, Japan

ABSTRACT This study evaluates the performance of decision tree model (XGBoost), time series model (LSTM), and semi-distributed hydrological model (HBV) in simulating daily runoff in a watershed with significant snowmelt contributions and intensive human activity. Daily runoff data from the Shiribeshi-Toshibetsu basin in Japan, spanning the period from 1998 to 2015, were used for model training, with data from 2016 to 2019 for validation. Comparative analysis reveals that the XGBoost model outperforms the LSTM and HBV models,achieving a Nash–Sutcliffe Efficiency (NSE) of 0.94, compared to 0.85 and 0.84 for the LSTM and HBV models, respectively. Additionally, Partial Dependence Plots (PDPs) were utilized to investigate the nonlinear impacts of climatic factors on runoff, identifying precipitation, average temperature, and wind speed as the most significant drivers. Specifically, the relationship between temperature and runoff follows a unique pattern: runoff initially increases with rising temperatures, then decreases, and subsequently increases again. These findings underscore the robustness of decision tree models in handling complex hydrological data and highlight the importance of understanding the intricate interactions between climatic variables and runoff. This study provides scientific support for water resource management in similar watersheds under changing climatic conditions.

Robust prediction for characteristics of digestion products in an industrial-scale biogas project via typical non-time series and time-series machine learning algorithms

Anaerobic digestion (AD) is a well-established pathway for treating agricultural organic waste, and machine learning has emerged as a novel tool to predict its product performance. In prior research, the majority of studies concentrated on non-time series models for laboratory-scale fermentation data. Consequently, the generalization performance of these models was significantly constrained, particularly in the context of industrial-scale biogas projects. Thus, in this study, typical non-time series models (GBR and RF) and time-series models (LSTM, CNN-LSTM, and DA-LSTM) after hyperparameter optimization were chosen to accurately predict the characteristics of digestion products in a biogas project. The ideal GBR model for CH4 content was obtained, and the R2 values of the test set and training set were 0.93 (RMSE=1.11) and 0.97 (RMSE=0.69), respectively. Temperature was the most important parameter for biogas production according to feature importance and SHAP analysis of the RF model. The DA-LSTM was superior to LSTM and CNN-LSTM for the prediction of biogas production, and the R2 of DA-LSTM was 0.87 (RMSE=1048.14) with a seq_len of 10 d. This study provides direction for high-efficiency biogas production in wet biogas projects with the aid of reliable machine learning models.

Time-series bidirectional adjustable N-soft expert MABAC method and its application for multi-attribute group decision-making

In hybrid models of soft expert sets, experts express only agreed or disagreed opinions about existing grades. This paper proposes a time-series bidirectional adjustable N-soft expert set model to address the shortcomings of existing models that cannot adjust existing grades to a more reasonable state or describe decision problems involving different times. Firstly, this model can explain the experts’ uncertain opinions and make positive or negative adjustments about existing grades. Secondly, the model contains information about time, describes dynamic multi-attribute group decision-making problems and explores objects’ changes and developments over time. And some related operations and propositions are derived. In addition, a new method called the bidirectional adjustable N-soft expert MABAC (multi-attributive border approximation area comparison) is proposed. On the one hand, the proposed method uses deviation maximizing and exponential decay methods to determine the time weights, ensuring the reliability of the time weights. On the other hand, it ranks objects based on their distances from an approximate boundary region, limiting the unconditional compensation among attribute values. Finally, this paper presents an example to verify its effectiveness and reliability by results analysis, sensitivity analysis, and comparison analysis.

Predicting Solar Cycles with a Parametric Time Series Model

Predicting Solar Cycles with a Parametric Time Series Model

Refining Estimation of the Instantaneous Reproduction Number During Early Pandemic Stages: Addressing Case-Reporting Variability and Serial Interval Uncertainty.

During infectious disease outbreaks, estimates for the instantaneous reproduction number, R(t), are essential for understanding transmission dynamics. This study develops and analyzes new methodology to improve estimation of R(t) when observed case counts are subject to reporting patterns and available serial interval estimates are subject to uncertainty and non-representativeness. Specifically, we developed a Bayesian time-since-infection model with layers to adjust for reporting measurement error, integrate multiple candidate serial interval estimates, and estimate transmission with an autoregressive time-series model incorporating factors relevant to transmission. Additionally, we provide practical tools to identify reporting patterns and determine when to smooth case counts for more usable R(t) estimates. We evaluated model performance relative to widely adopted methodology by simulating outbreak data, finding improved R(t) estimation with the proposed methodology. We also used 2020 COVID-19 data to analyze transmission trends and predictors, identifying strong day-of-week and social distancing effects that subsequently reduced estimate volatility. In addition to new approaches for addressing serial interval uncertainty and incorporating transmission predictor information, this study provides an alternative approach for addressing case-reporting patterns without delaying detection or smoothing over relevant transmission signals. These tools and findings may be used or built upon for current and future outbreaks.

Pollutants-mediated viral hepatitis in different types: assessment of different algorithms and time series models

The escalating frequency of environmental pollution incidents has raised significant concerns regarding the potential health impacts of pollutant fluctuations. Consequently, a comprehensive study on the role of pollutants in the prevalence of viral hepatitis is indispensable for the advancement of innovative prevention strategies. Monthly incidence rates of viral hepatitis from 2005 to 2020 were sourced from the Chinese Center for Disease Control and Prevention Infectious Disease Surveillance Information System. Pollution data spanning 2014–2020 were obtained from the National Oceanic and Atmospheric Administration (NOAA), encompassing pollutants such as CO, NO2, and O3. Time series analysis models, including seasonal auto-regressive integrated moving average (SARIMA), Holt-Winters model, and Generalized Additive Model (GAM), were employed to explore prediction and synergistic effects related to viral hepatitis. Spearman correlation analysis was utilized to identify pollutants suitable for inclusion in these models. Concurrently, machine learning (ML) algorithms were leveraged to refine the prediction of environmental pollutant levels. Finally, a weighted quantile sum (WQS) regression framework was developed to evaluate the singular and combined impacts of pollutants on viral hepatitis cases across different demographics, age groups, and environmental strata. The incidence of viral hepatitis in Beijing exhibited a declining trend, primarily characterized by HBV and HCV types. In predicting hepatitis prevalence trends, the Holt-Winters additive seasonal model outperformed the SARIMA multiplicative model ((1,1,0) (2,1,0) [12]). In the prediction of environmental pollutants, the SVM model demonstrated superior performance over the GPR model, particularly with Polynomial and Besseldot kernel functions. The combined pollutant risk effect on viral hepatitis was quantified as βWQS (95% CI) = 0.066 (0.018, 0.114). Among different groups, PM2.5 emerged as the most sensitive risk factor, notably impacting patients with HCV and HEV, as well as individuals aged 35–64. CO predominantly affected HAV patients, showing a risk effect of βWQS (95% CI) = − 0.0355 (− 0.0695, − 0.0016). Lower levels of PM2.5 and PM10 were associated with heightened risk of viral hepatitis incidence with a lag of five months, whereas elevated levels of PM2.5 (100–120 μg/m3) and CO correlated with increased hepatitis incidence risk with a lag of six months. The Holt-Winters model outperformed the SARIMA model in predicting the incidence of viral hepatitis. Among machine learning algorithms, SVM and GPR models demonstrated superior performance for analyzing pollutant data. Patients infected with HAV and HEV were primarily influenced by PM10 and CO, whereas SO2 and PM2.5 significantly impacted others. Individuals aged 35–64 years appeared particularly susceptible to these pollutants. Mixed pollutant exposures were found to affect the development of viral hepatitis with a notable lag of 5–6 months. These findings underscore the importance of long-term monitoring of pollutants in relation to viral hepatitis incidence.

Forecasting of Tomato Price in Karnataka using BATS Model

Tomato plays a vital role in Karnataka’s agro-processing and food industries, contributing significantly to the state’s economy. Even though tomato production in Karnataka is substantial, the state’s market is characterized by price volatility. Tomato prices can undergo drastic fluctuations within short time periods, posing severe challenges to the farmers and consumers. To address these problems, time series models, such as Exponential Smoothing, ARIMA, SARIMA, BATS and TBATS have been implemented to forecast the tomato prices in Kolar market of Karnataka state using monthly wholesale prices data from the year 2010 to 2022. Among the applied models, BATS showed superior performance in terms of model validation criteria such as Root Mean Square Error and Mean Absolute Percentage Error.

The Impact of Inflation on the Performance of Stock Markets in the Gulf Cooperation Council Countries

This study explores the interplay between inflation and stock markets in Gulf Cooperation Council (GCC) countries from the 1990s to the early 21st century. Utilizing a PANEL time series model and Autoregressive Distributed Lag (ARDL) approach, we focus on the financial sector, emphasizing stock market performance. Our analysis of annual data reveals a significant, long-term impact of inflation on GCC stock markets, emphasizing a reciprocal relationship. We observe stability in this impact and identify potential variations among GCC countries. The study contributes valuable insights into the economic dynamics of the GCC region, with implications for investors, policymakers, and researchers navigating the intricate connection between inflation and stock market behavior.

An Improved Spatiotemporal Time Series Modelling Procedure with Application to Forecasting of Solar Radiation

The demand for energy and associated services to meet sustainable agricultural and economic growth and improve human health and lifestyle is increasing day by day. Hence, there is a need for systematic and scientific prediction of solar and other renewable sources of energy to meet these requirements. The main purpose of this study is to propose a hybrid Space-Time Autoregressive Moving Average Artificial Neural Network (STARMA-ANN) model for the precise and accurate forecasting of solar radiation for better planning and policy making. This approach has been implemented at seven geographical locations of Bihar in India. Spatial weight matrices have been used to describe all seven geographical locations and incorporated into the STARMA model to reflect the spatial and temporal correlation. To deal with nonlinear dynamics in the spatiotemporal data, ANN technique has been applied on residuals of the fitted STARMA model. The results have demonstrated that the proposed hybrid model performs better prediction accuracy than using conventional STARMA model, especially for spatiotemporal data with nonlinear characteristics of solar radiation.

Designing of Airspeed Measurement Method for UAVs Based on MEMS Pressure Sensors.

Airspeed measurement is crucial for UAV control. To achieve accurate airspeed measurements for UAVs, this paper calculates airspeed data by measuring changes in air pressure and temperature. Based on this, a data processing method based on mechanical filtering and the improved AR-SHAKF algorithm is proposed to indirectly measure airspeed with high precision. In particular, a mathematical model for an airspeed measurement system was established, and an installation method for the pressure sensor was designed to measure the total pressure, static pressure, and temperature. Secondly, the measurement principle of the sensor was analyzed, and a metal tube was installed to act as a mechanical filter, particularly in cases where the aircraft has a significant impact on the gas flow field. Furthermore, a time series model was used to establish the sensor state equation and the initial noise values. It also enhanced the Sage-Husa adaptive filter to analyze the unavoidable error impact of initial noise values. By constraining the range of measurement noise, it achieved adaptive noise estimation. To validate the superiority of the proposed method, a low-complexity airspeed measurement device based on MEMS pressure sensors was designed. The results demonstrate that the airspeed measurement device and the designed velocity measurement method can effectively calculate airspeed with high measurement accuracy and strong interference resistance.

Development and Comparison of InSAR-Based Land Subsidence Prediction Models

Land subsidence caused by human engineering activities is a serious problem worldwide. We selected Qian’an County as the study area to explore the evolution of land subsidence and predict its deformation trend. This study utilized synthetic aperture radar interferometry (InSAR) technology to process 64 Sentinel-1 data covering the area, and high-precision and high-resolution surface deformation data from January 2017 to December 2021 were obtained to analyze the deformation characteristics and evolution of land subsidence. Then, land subsidence was predicted using the intelligence neural network theory, machine learning methods, time-series prediction models, dynamic data processing techniques, and engineering geology of ground subsidence. This study developed three time-series prediction models: a support vector regression (SVR), a Holt Exponential Smoothing (Holt) model, and multi-layer perceptron (MLP) models. A time-series prediction analysis was conducted using the surface deformation data of the subsidence funnel area of Zhouzi Village, Qian’an County. In addition, the advantages and disadvantages of the three models were compared and analyzed. The results show that the three developed time-series data prediction models can effectively capture the time-series-related characteristics of surface deformation in the study area. The SVR and Holt models are suitable for analyzing fewer external interference factors and shorter periods, while the MLP model has high accuracy and universality, making it suitable for predicting both short-term and long-term surface deformation. Ultimately, our results are valuable for further research on land subsidence prediction.

Explaining state efforts to create Total Maximum Daily Load (TMDL) agreements

AbstractObjectivesWith the rejuvenated emphasis on nonpoint pollution under the Water Quality Act (WQA) of 1987, Environmental Protection Agency (EPA) began to face an onslaught of lawsuits designed to pressure the EPA to enforce the requirements of Section 319 of the WQA to address nonpoint pollution. Known as Total Maximum Daily Load (TMDL) agreements, the purpose of these plans was to limit the amount of polluted runoff reaching a state's waterways. While some states took a proactive stance on these plans, other states resisted the implementation of Section 319. This article seeks to understand state choices in the development and implementation of TMDL agreements.MethodsUtilizing a data set spanning state‐level data from 2000 to 2020, we test a novel cross‐sectional time series model employing the number agreements entered into by a state as the dependent variable.ResultsWe find that both political and need explanations are generally supported, while policy need explanations are somewhat more promising.ConclusionsTaken together, the models offer several insights into state choices around TMDL creation. The political model is the weakest, suggesting that TMDLs are not overtly political. Policy needs seem to play a more critical role in the preponderance of TMDL agreements than partisan politics.

Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation

Considering that investing in the production of corn and soybeans is conditioned by production costs and several risks, the objective of this research work was to develop a simulation model for the prediction of the production costs of these commodities, considering the variability and correlation of key variables. The descriptive analysis of the data focused on measures such as mean, standard deviation, and coefficient of variation. To evaluate the relationship between commodity and input prices, Spearman’s demonstration coefficient and the coefficient of determination (R2) were used. A Monte Carlo simulation (MCS) was used to evaluate the variation in production costs and net revenues. The Predictor tool was used to make predictions based on historical data and time series models. This study was made for the period between 2018 and 2022 based on data provided by fifty companies from the state of São Paulo, Brazil. The results showed that the production cost/ha of corn faces a high-cost risk, particularly when production and market conditions are characterized by high levels of volatility, uncertainty, complexity, and ambiguity. The model proposed forecasts prices more accurately, as it considers the variation in the costs of inputs that most significantly influence the costs of corn and soybean crops.

Assessing the accuracy of directional forecasts

ABSTRACT Directional predictions are used in a variety of settings, including economics, finance, meteorology and medicine. Since the objectives in each setting can be quite different, appropriate tests for assessing the predictions are necessary. After summarizing existing tests, we propose new tests to assess the accuracy for cases where getting extreme events wrong is particularly costly and where there is serial dependence in small samples. We show in simulations that the latter test has a more favourable power-size trade-off relative to existing tests. The new tests are then applied to the directional EUR/USD forecasts in the ifo-Institute’s World Economic Survey and to the point forecasts in the Philadelphia Fed’s Survey of Professional Forecasters. For the former, the new tests show that while there is profitability and predictive accuracy in some cases, a simple time-series model is not beaten. For the latter, extreme values are not the main driver of the predictive value up to two quarters ahead. This shows that the additional tests allow a more precise determination of predictive accuracy and economic value and where it is coming from.

A time series modelling and forecasting of under-5 mortality rate in Nigeria

A time series modelling and forecasting of under-5 mortality rate in Nigeria

Drafting restraint: Are military recruitment policies associated with interstate conflict initiation?

Are countries that use conscription more restrained in their use of military force? A common argument holds that military conscription restrains leaders from using force because it increases the political cost of war and distributes them more evenly and broadly across the population. Despite this intuition, empirical evidence to support it is at best inconclusive. This article introduces a novel perspective on the relationship between military recruitment (MR) policies and conflict initiation (CI) by arguing that the military’s size relative to society – its military participation rate (MPR) – is an important and overlooked part of this story. MPR is a more direct measure of the population’s exposure to the costs of war, but high MPR may also increase CI by enhancing military capacity. By incorporating MPR into the analysis of CI, both independently and in interaction with conscription, this article provides a more comprehensive understanding of how MR practices shape CI. It tests these new hypotheses about the relationship between MPR, conscription and CI using a variety of time-series models that cover all country-years from 1816 to 2011. The findings do not support the conventional wisdom, instead revealing that neither conscription nor volunteerism is independently associated with restrained initiation of military conflicts abroad. On the contrary, these recruitment practices are more likely to be associated with an increase in the likelihood of CI. These findings indicate that we should be skeptical of traditional arguments that assume conscription leads to restraint in the use of force, either independently or conditional on MPR. These counterintuitive results underscore the need for additional research on the complex relationship between MR practices, civil–military relations and foreign policy.

Time-variations of wave energy and forecasting power availability at a site in Fiji using time-series, regression and ANN techniques

ABSTRACT Recently, there has been a shift in the global energy landscape to move to reliable, clean, and eco-friendly renewable energy sources to address global issues such as climate change and greenhouse gas emissions. One such energy source is wave energy; researchers attempt to develop models that can accurately forecast the availability of wave energy as an alternative energy source. In this paper, an Artificial Neural Network (ANN) model along with statistical models such as time series models, and regression models are proposed for forecasting wave energy at a site in Fiji using the wave height and wave period as the independent variables. The performance of the proposed models developed is compared using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the goodness-of-fit ( R 2 ) value. The proposed model is then further benchmarked with the naïve model. The empirical results reveal that the proposed ANN model outclassed all the other models and was more efficient and accurate in forecasting wave energy than the regression and time series models. By accurate wave modelling and by incorporating impedance matching, maximum power generation can be achieved.