Multiplicative Decomposition Model Research Articles

Seasonality of influenza-like illness and short-term forecasting model in Chongqing from 2010 to 2022

BackgroundInfluenza-like illness (ILI) imposes a significant burden on patients, employers and society. However, there is no analysis and prediction at the hospital level in Chongqing. We aimed to characterize the seasonality of ILI, examine age heterogeneity in visits, and predict ILI peaks and assess whether they affect hospital operations.MethodsThe multiplicative decomposition model was employed to decompose the trend and seasonality of ILI, and the Seasonal Auto-Regressive Integrated Moving Average with exogenous factors (SARIMAX) model was used for the trend and short-term prediction of ILI. We used Grid Search and Akaike information criterion (AIC) to calibrate and verify the optimal hyperparameters, and verified the residuals of the multiplicative decomposition and SARIMAX model, which are both white noise.ResultsDuring the 12-year study period, ILI showed a continuous upward trend, peaking in winter (Dec. - Jan.) and a small spike in May-June in the 2–4-year-old high-risk group for severe disease. The mean length of stay (LOS) in ILI peaked around summer (about Aug.), and the LOS in the 0–1 and ≥ 65 years old severely high-risk group was more irregular than the others. We found some anomalies in the predictive analysis of the test set, which were basically consistent with the dynamic zero-COVID policy at the time.ConclusionThe ILI patient visits showed a clear cyclical and seasonal pattern. ILI prevention and control activities can be conducted seasonally on an annual basis, and age heterogeneity should be considered in the health resource planning. Targeted immunization policies are essential to mitigate potential pandemic threats. The SARIMAX model has good short-term forecasting ability and accuracy. It can help explore the epidemiological characteristics of ILI and provide an early warning and decision-making basis for the allocation of medical resources related to ILI visits.

Research on the Impact of Energy Price on Carbon Emission Intensity of China—An Empirical Study Based on LMDI Decomposition and Econometric Models

Taking the data of various sectors and three industries from 1980 to 2019 as the research object, the LMDI-I (Logarithmic Mean Divisia Index) multiplicative decomposition model, which is based on the principle of decomposing the change in energy consumption into the contribution of each factor, was used to decompose the carbon emission intensity into technological progress effect and economic structure changing effect. Meanwhile, quantitative econometric models of energy price, economic growth, energy consumption structure, and the two effects were also established. The empirical results showed that energy price, economic growth, and energy consumption structure significantly influenced the reduction in carbon emission intensity. A positive U-shaped relationship between energy prices and carbon emission intensity was overserved, and the rise of energy prices mainly drive the decline of carbon emission intensity through the effect of technological progress. However, the effect of economic structure driven by the rise of energy prices was limited; thus, further optimization of economic structure is needed. Additionally, the proportion of coal consumption was positively correlated with the technological progress effect and economic structure change effect, while the decrease in coal consumption proportion promoted the decline of carbon emission intensity. Finally, three recommendations based on the analysis were proposed.

A comparative study of univariate models for container throughput forecasting of major ports in Asia

This study aims to make comparisons on different univariate forecasting methods and provides a more accurate short-term forecasting model on the container throughput for rendering a reference to relevant authorities. We collected monthly data regarding container throughput volumes for three major ports in Asia, Shanghai, Singapore, and Busan Ports. Six different univariate methods, including the grey forecasting model, the hybrid grey forecasting model, the multiplicative decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, and the seasonal autoregressive integrated moving average (SARIMA) model, were used. We found that the hybrid grey forecasting model outperforms the other univariate models. This study’s findings can provide a more accurate short-term forecasting model for container throughput to create a reference for port authorities.

Forecasting Enrolment Data of Surigao del Sur State University, Philippines using Regression Analysis and Multiplicative Decomposition Model

The study endeavored to predict the number of students who will enroll in Surigao del Sur State University using the historic data from 2010-2015. It was accomplished to utilize two existing methods to form its model in forecasting the enrolment data of the University. To project the number of students enrolled at the university, regression analysis, and a multiplicative decomposition model is used. The Mean Absolute Percentage Error (MAPE) of the forecasted enrolment data was 3.10 percent, according to the results. This only demonstrated that the estimated data were similar to the actual data, implying that it is accurate. The predicted enrolment data can be used to support decision-making and as an input to the University Development Plan.

Forecasting Enrolment Data of Surigao del Sur State University, Philippines using Regression Analysis and Multiplicative Decomposition Model

The study endeavored to predict the number of students who will enroll in Surigao del Sur State University using the historic data from 2010-2015. It was accomplished to utilize two existing methods to form its model in forecasting the enrolment data of the University. To project the number of students enrolled at the university, regression analysis, and a multiplicative decomposition model is used. The Mean Absolute Percentage Error (MAPE) of the forecasted enrolment data was 3.10 percent, according to the results. This only demonstrated that the estimated data were similar to the actual data, implying that it is accurate. The predicted enrolment data can be used to support decision-making and as an input to the University Development Plan.

Forecasting of municipal solid waste generation using non-linear autoregressive (NAR) neural models

Municipal solid waste (MSW) generation is a multi-variable dependent process and hence its quantification is relatively not easy. The estimations for monthly MSW generation are required to provide theoretical guidelines for understanding and designing the disposal system. These estimations help in budgetary planning for the handling of future waste with optimized waste management system. This study forecasts the monthly MSW generation in Nagpur (India) for the year 2023 using non-linear autoregressive (NAR) models. The classical multiplicative decomposition model with simple linear regression in time series was constructed with maximum absolute error of 6.34% to overcome the problem of data availability. It was observed that NAR neural models were able to predict short-term monthly MSW generation with absolute maximum error of 6.45% (Model A) and 3.05% (Model B) for the observation period. It was also concluded that the variation in MSW generation was best captured when yearly lagged values were used for the construction of NAR model and coefficient of efficiency (E) was 0.99 and 0.97 during testing and validation, respectively. It was found that in the year 2023, the city will record minimum waste generation in the month of February and maximum in the month of December. For the year 2023, it had been estimated that the maximum 48504 ± 1569 tons of waste in December and minimum 39682 ± 471 tons in February will be generated. It had also been estimated that the minimum waste generation from the year 2017 to 2023 will increase by approximately 5345 tons.

Modified multiplicative decomposition model for tissue growth: Beyond the initial stress-free state

The multiplicative decomposition model is widely employed for predicting residual stresses and morphologies of biological tissues due to growth. However, it relies on the assumption that the tissue is initially in a stress-free state, which conflicts with the observations that any growth state of a biological tissue is under a significant level of residual stresses that helps to maintain its ideal mechanical conditions. Here, we propose a modified multiplicative decomposition model in which the initial state (or reference configuration) of a biological tissue is endowed with a residual stress instead of being stress-free.Releasing theoretically the initial residual stress, the initially stressed state is first transmitted into a virtual stress-free state, thus resulting in an initial elastic deformation. The initial virtual stress-free state subsequently grows to another counterpart with a growth deformation, and the latter is further integrated into its natural configuration of a real tissue with an excessive elastic deformation that ensures tissue compatibility. With this decomposition, the total deformation arising during growth may be expressed as the product of elastic deformation, growth deformation and initial elastic deformation, while the corresponding free energy density should depend on the initial residual stress and the total deformation. Three key issues including the explicit expression of the free energy density, the predetermination of the initial elastic deformation, and the initial residual stress are addressed.Finally, we consider a tubular organ as a representative example to demonstrate the effects of the proposed initial residual stress on stress distribution and on shape formation through an incremental stability analysis. Our results suggest that the initial residual stress exerts a major influence on the growth stress and the morphology of biological tissues. The model bridges the gap between any two growth states of a biological tissue that is endowed with a certain level of residual stresses.

Case Study: Analysis and Predictions of Shipment Load in Festive Season

A time-phased forecasting in rest of the year has a huge impact shipping costs, however during a festive season of the year, well predicted and analyzed re-engineering of shipment load plays a major role in bringing up sales. The major concern of the customer is to get delivery on-time, whereas that of the wholesaler / retailer is to provide delivery without any complaint in order to retain the customer. In the framework of competitive supply chain market, necessary accurate Shipping load forecasting tools are required. With the focus of improving prediction accuracy, this case study presents use of Time-series models, multiplicative decomposition model (MDM) and smoothening techniques, on shipping load demand of Arora-Ludhiana-Handlooms during festive seasons for short-term forecasting.

Detrending crop yield data for spatial visualization of drought impacts in the United States, 1895–2014

Historical drought events have had severe impacts on United States agriculture, but attempts to quantify and compare these impacts across space and time have been challenging because of the nonlinear and non-stationary nature of the crop yield time series. Here, we address this challenge using long-term state- and county-level corn yield data from 1895 to 2014. We apply and compare six trend simulation models – simple linear regression, second order polynomial regression, centered moving average, locally weighted regression, spline smoothing, and empirical mode decomposition – to simulate the nonlinear trend, and two decomposition models – an additive decomposition model and a multiplicative decomposition model – to remove the nonlinear trend from the yield time series. Our comparison of each method evaluates their respective advantages and disadvantages with respect to applicability across time and space, efficiency, and robustness. We find that a locally weighted regression model, coupled with a multiplicative decomposition model, is the most appropriate data self-adaptive detrending method. Detrended crop yield minus one represents the percentage lower or higher than normal yield conditions, termed “crop yield anomaly”. We then apply this detrending method and perform correlation analysis to show the quantitative relationship between state-level corn yield anomalies and multiple drought indices. We find that the 3-month Standardized Precipitation Index (SPI) in August and Palmer Z-index in July correlate most closely with corn yield anomalies. This correlation is higher east of the 100° W meridian, where irrigation is not as extensively used. Finally, we show how the detrending process allows spatial visualization of drought impact on corn yield in the US using gridded August 3-month SPI values with examples from six major droughts. Our focus on comparing detrending methods produces a methodology that can aid analysis of agricultural yield for both empirical and modeling studies connecting environmental and climate conditions to crop productivity.

Extracting environmentally driven growth trends from diameter increment series based on a multiplicative decomposition model

The study developed a conceptual framework for partitioning the components of diameter increment to potentially detect the influence of environmental changes. This process consisted of two steps. First, a multiplicative decomposition diameter increment model was introduced to evaluate the influence of ageing, site quality, competition status, and thinning effects on individual tree growth. Second, generalized additive models were applied to identify the nonlinear dynamic of growth trends caused by environmental changes. The conceptual framework was then applied to Norway spruce ( Picea abies (L.) Karst.) growing in southwest Germany. The database consisted primarily of tree ring series collected from trees cut from long-term experimental stands. Also, stand-level data were available from periodical remeasurements of these plots. The developed analytical technique effectively removed non-environment-related effects (ageing, site quality, and stand dynamic) from the growth signal provided in the diameter increment series. Growth trends deducted from estimates based on either nonlinear least squares, generalized nonlinear least squares, or nonlinear mixed-effects approaches displayed quite similar patterns. In general, the trend in diameter increment showed a long-term increase from the 1920s into the 1990s with a midterm depression in the 1940s that was followed by a significant decrease in the recent past.

A statistical fitting model in Taiwan’s stock price

By using the time series of stock price, we apply the multiplicative decomposition model that decomposed into the trend, seasonal and cyclical factors to fit the stock price of high technical electronic industry in Taiwan. Seasonality is an important feature of these series that we make two methods handling. The Method II of seasonal factor is better than Method I. The main results of this study demonstrate that the model is quite satisfactory.