Seasonal Adjustment Factors Research Articles

The challenges of seasonal adjustment for the Current Employment Statistics survey during the COVID-19 pandemic

For its seasonally adjusted data, the Bureau of Labor Statistics (BLS) seeks to remove all fluctuations that are caused by the yearly cycle of seasons. The coronavirus 2019 pandemic posed a challenge to this task, because its effect was strong, across the entire economy, and lasted for at least several months. To ensure that the effects of the pandemic were not being incorporated into the seasonal adjustment factors, the Current Employment Statistics program took additional actions such as splitting the seasonal adjustment into two runs (prepandemic and postpandemic) and incorporating additional types of outliers into its models.

Multi-data source-based recycling value estimation of wasted domestic electrical storage water heater in China

A domestic electrical storage water heater (i.e., DESWH) is one of the 14 products listed in China’s Waste Electrical and Electronic Products Disposal Catalogue (Batch 2). Due to the lack of systematic quantitative analysis on the waste quantity and recovery value of a DESWH, a multi-data source-based hybrid methodology based on quarterly sales data, survey data and internet data is proposed. In the methodology, the seasonal Mann-Kendall trend test is used to identify the seasonal trait of the quarterly sales data for DESWHs, and an accurate prediction of the sales volume of DESWHs is obtained via a generalised seasonal grey model with dynamic seasonal adjustment factors. Then the lifespan distribution of DESWHs is fitted based on the survey data, and the quantity of wasted DESWHs is estimated from 2012Q1 to 2038Q4. Finally, on the basis of the data crawled from the internet, the weight distribution of DESWHs is constructed, and the recycling value of wasted DESWHs is then calculated. The empirical results show that the waste quantity of DESWHs will increase greatly from 2012Q1 to 2022Q4 and that the recycling value of wasted DESWHs may be worth 18.48 billion yuan in China. The results show that the wasted DESWHs have a great recycling value, and that the proposed multi-data source based hybrid methodology can be used as an effective estimation method for the recycling value of electronic waste.

Forecasting air quality in China using novel self-adaptive seasonal grey forecasting models

PurposeAiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal fluctuation information about the air quality index. Based on the novel self-adaptive seasonal adjustment factor, the novel seasonal grey forecasting models are established to predict the air quality in China.Design/methodology/approachThis paper constructs a novel self-adaptive seasonal adjustment factor for quantifying the seasonal difference information of air quality. The novel self-adaptive seasonal adjustment factor reflects the periodic fluctuations of air quality. Therefore, it is employed to optimize the data generation of three conventional grey models, consisting of the GM(1,1) model, the discrete grey model and the fractional-order grey model. Then three novel self-adaptive seasonal grey forecasting models, including the self-adaptive seasonal GM(1,1) model (SAGM(1,1)), the self-adaptive seasonal discrete grey model (SADGM(1,1)) and the self-adaptive seasonal fractional-order grey model (SAFGM(1,1)), are put forward for prognosticating the air quality of all provinces in China .FindingsThe experiment results confirm that the novel self-adaptive seasonal adjustment factors promote the precision of the conventional grey models remarkably. Simultaneously, compared with three non-seasonal grey forecasting models and the SARIMA model, the performance of self-adaptive seasonal grey forecasting models is outstanding, which indicates that they capture the seasonal changes of air quality more efficiently.Research limitations/implicationsSince air quality is affected by various factors, subsequent research may consider including meteorological conditions, pollutant emissions and other factors to perfect the self-adaptive seasonal grey models.Practical implicationsGiven the problematic air pollution situation in China, timely and accurate air quality forecasting technology is exceptionally crucial for mitigating their adverse effects on the environment and human health. The paper proposes three self-adaptive seasonal grey forecasting models to forecast the air quality index of all provinces in China, which improves the adaptability of conventional grey models and provides more efficient prediction tools for air quality.Originality/valueThe self-adaptive seasonal adjustment factors are constructed to characterize the seasonal fluctuations of air quality index. Three novel self-adaptive seasonal grey forecasting models are established for prognosticating the air quality of all provinces in China. The robustness of the proposed grey models is reinforced by integrating the seasonal irregularity. The proposed methods acquire better forecasting precisions compared with the non-seasonal grey models and the SARIMA model.

Predictions and mitigation strategies of PM2.5 concentration in the Yangtze River Delta of China based on a novel nonlinear seasonal grey model.

High delicate particulate matter (PM2.5) concentration can seriously reduce air quality, destroy the environment, and even jeopardize human health. Accordingly, accurate prediction for PM2.5 plays a vital role in taking precautions against upcoming air ambient pollution incidents. However, due to the disturbance of seasonal and nonlinear characteristics in the raw series, pronounced forecasts are confronted with tremendous handicaps, even though for seasonal grey prediction models in the preceding researches. A novel seasonal nonlinear grey model is initially designed to address such issues by integrating the seasonal adjustment factor, the conventional Weibull Bernoulli grey model, and the cultural algorithm, simultaneously depicting the seasonality and nonlinearity of the original data. Experimental results from PM2.5 forecasting of four major cities (Shanghai, Nanjing, Hangzhou, and Hefei) in the YRD validate that the proposed model can obtain more accurate predictive results and stronger robustness, in comparison with grey prediction models (SNGBM(1,1) and SGM(1,1)), conventional econometric technology (SARIMA), and machine learning methods (LSSVM and BPNN) by employing accuracy levels. Finally, the future PM2.5 concentration is forecasted from 2020 to 2022 using the proposed model, which provides early warning information for policy-makers to develop PM2.5 alleviation strategies.

GIS-Based Multivariate Spatial Clustering for Traffic Pattern Recognition using Continuous Counting Data

Traffic count stations play a key role in measuring roadway characteristics and traffic performance by collecting and monitoring travel behavior and vehicle data. Continuous counting stations (CCSs), which count traffic volumes continuously throughout the year, are used to develop seasonal adjustment factors to convert short-term traffic counts (average daily traffic) to annual average daily traffic (AADT). As data collection is conducted at limited locations, many state Departments of Transportation (DOTs) group the CCSs based on different traffic patterns and estimate the AADT at specific locations by considering seasonal adjustment factors. Computer-based clustering approaches are widely used in grouping continuous traffic data for their accuracy in traffic pattern recognition. However, most of the clustering techniques do not consider the spatial constraints and therefore overlooked the locational proximity and inference from nearby traffic data. In this study, a GIS-based multivariate spatial clustering approach was developed to recognize statewide traffic patterns based on temporal and spatial variables. A total of 12 optimal clusters were automatically computed and labeled based on the proposed clustering algorithm. The proposed clustering approach was compared and validated based on machine learning classifiers. The results showed that it outperformed the traditional Michigan DOT clustering approach and was consistent in nature across different years. The model was applied to estimate the AADT, and good accuracy was detected relative to other approaches. The proposed clustering method offers a new approach to group traffic patterns by simultaneously incorporating proximity and similarity of traffic data.

Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors

Due to influences of natural and social factors, the data of solar energy consumption generally show the characteristic of seasonal fluctuations. In order to forecast data with seasonal fluctuation, this study proposed a novel seasonal grey model with dynamic seasonal adjustment factors, the factors reflect the proportion of the actual value deviating from the average trend value due to seasonal effects. The proposed model, grey model, seasonal grey model and a seasonal autoregressive integrated moving average model were built based on monthly data of the industrial solar energy consumption in the United States from 2005 to 2017. The results demonstrate that the grey model only show a growth trend of the original data. The seasonal autoregressive integrated moving average model and seasonal grey model can identify and predict the seasonal characteristics of series, but fail to accurately forecast their fluctuation effects. Moreover, the proposed model is able to effectively identify dynamic change process of seasonal adjustment factors and significantly improves prediction accuracy. The predicted results showed that the year-on-year growth rate of monthly consumption of the industrial solar energy in the United States maintained at 40–50% in 2018–2019, and the monthly consumption still had obvious periodic seasonal fluctuation characteristics.

Estimating Turning Movements at Signalized Intersections Using Artificial Neural Networks

Intersection turning movements’ counts are critical input data for traffic studies, analysis, and forecasting. These types of counts are often used to analyze operational performance of signalized intersections under different traffic conditions and peak hours. There are several methods to collect these movements, such as manual counts and video image processing. Traffic studies require turning movement counts although they may not be readily available, especially in the case of traffic forecasting or when seasonal adjustment factors are applied. In these cases, only approach volumes might be available and turning movements are assumed using different techniques to balance the inbound and outbound traffic. Nonetheless, these techniques require initial conditions, and their outcomes are highly sensitive to the initial assumptions. In this paper, a robust and practical method is developed. The turning movement counts at 691 four-leg, and 156 three-leg signalized intersections are analyzed to develop an accurate and reliable turning movement estimation model. A total of 4,175 hours of turning movement counts are used. An artificial neural networks (ANN) model is trained to analyze the relationship between the approach volumes and the corresponding turning movements. The results show that the developed ANN model can be utilized to predict turning movements at a high level of accuracy.

Estimation of Seasonal Correction Factors for Indoor Radon Concentrations in Korea.

Long-term exposure to high radon concentration exerts pathological effects and elicits changes in respiratory function, increasing an individual’s risk of developing lung cancer. In health risk assessment of indoor radon, consideration of long-term exposure thereto is necessary to identify a relationship between indoor radon exposure and lung cancer. However, measuring long-term indoor radon concentration can be difficult, and a statistical model for predicting mean annual indoor radon concentrations may be readily applicable. We investigated the predictability of mean annual radon concentrations using national data on indoor radon concentrations throughout the spring, summer, fall, and winter seasons in Korea. Indoor radon concentrations in Korea were highest in the winter and lowest in the summer. We derived seasonal correction and seasonal adjustment factors for each season based on the method proposed by previous study. However, these factors may not be readily applicable unless measured in a specific season. In this paper, we separate seasonal correction factors for each month of the year (new correction factors) based on correlations between indoor radon and meteorological factors according to housing type. To evaluate the correction factors, we assessed differences between estimated and measured mean annual radon concentrations. Roughly 97% of the estimated values were within ±40 Bq/m3 of actual measured values in detached houses, and roughly 85–87% of the estimated values were within ±40 Bq/m3 of the measured values in other residences. In most cases, the seasonal correction factors and the new correction factors had slightly better agreement than the seasonal adjustment factor. For predicting mean annual radon concentrations, the seasonal correction factors or seasonal adjustment factors can be of use when actual measurements of indoor radon concentrations for a specific season are available. Otherwise, the new correction factors may be more readily applicable.

Real-Time Prediction of Seasonal Heteroscedasticity in Vehicular Traffic Flow Series

Over the past decade, traffic heteroscedasticity has been investigated with the primary purpose of generating prediction intervals around point forecasts constructed usually by short-term traffic condition level forecasting models. However, despite considerable advancements, complete traffic patterns, in particular the seasonal effect, have not been adequately handled. Recently, an offline seasonal adjustment factor plus GARCH model was proposed in Shi et al. 2014 to model the seasonal heteroscedasticity in traffic flow series. However, this offline model cannot meet the real-time processing requirement proposed by real-world transportation management and control applications. Therefore, an online seasonal adjustment factors plus adaptive Kalman filter (OSAF+AKF) approach is proposed in this paper to predict in real time the seasonal heteroscedasticity in traffic flow series. In this approach, OSAF and AKF are combined within a cascading framework, and four types of online seasonal adjustment factors are developed considering the seasonal patterns in traffic flow series. Empirical results using real-world station-by-station traffic flow series showed that the proposed approach can generate workable prediction intervals in real time, indicating the acceptability of the proposed approach. In addition, compared with the offline model, the proposed online approach showed improved adaptability when traffic is highly volatile. These findings are important for developing real-time intelligent transportation system applications.

Comparison of evaporation rate on open water bodies: energy balance estimate versus measured pan

Abstract Much attention has been paid to establish accurately open water evaporation since the lake itself is the largest consumer of water. The aim of this study is to assess the discrepancy in the measured (pan evaporation) and estimated (Penman) evaporation rate, seasonally, based on the results from a 37-year energy budget analysis of Lake Burrumbeet, Australia. The detailed analysis of meteorological data showed that evaporation is fully radiation driven and that the effect of wind is minimal. Sensitivity analysis shows that evaporation estimation is more sensitive to shortwave radiation followed by relative humidity. An increase or decrease of estimated shortwave radiation by 10% could result in an increase or decrease of estimated evaporation up to 18%. The Penman combination method is relatively the least sensitive to wind speed but could bring a significant effect on the lake level fluctuation since a 10% increase of wind speed increases the estimated evaporation by 2.3%. The current analysis highlights the relative roles of radiation, temperature, humidity, and wind speed in modulating the rate of evaporation from the lake surface, by employing an inter-monthly seasonal adjustment factor to the estimated evaporation in the lake water budget analysis, with implications for the inter-monthly variability and short-term trends assessment of water resource through various meteorological parameters.

Wind Energy Predictions of Small-Scale Turbine Output Using Exponential Smoothing and Feed-Forward Neural Network

This article presents the comparisons of energy production predictions of a small-scale 40 kW wind turbine using an exponential smoothing technique and multilayer feed-forward neural network. For wind energy predictions, the developed mathematical model based on exponential smoothing was used to smoothen any seasonality arising in the time series data obtained at the site. This model was developed using three smoothing constant values of 0.20, 0.65, and 0.90, as well as a combination of a smoothing constant value of 0.90 with a seasonal adjustment factor for prediction of a small-scale wind turbine output for a period of 12 months. In addition, an energy model based on a multilayer feed-forward neural network was used to compute the energy generation of the turbine. The seasonally adjusted forecast model accurately predicted the wind energy output with the lowest forecast errors when compared to the chosen three smoothing constants. The energy forecasts obtained from the seasonal adjusted forecast model and multilayer feed-forward neural network were compared to the actual energy generation of the turbine at the considered tower height in terms of their forecast erroneous values. KeywordsTime Series Data (TSD); Smoothing and Seasonal Factor; Exponential Smoothing; Feed-Forward Neural Network (FNN), Small-Scale Wind Turbine

Determining the optimal number of seasonal adjustment factor groupings when estimating annual average daily traffic and investigating their characteristics

Although cluster analysis is recommended by the US Traffic Monitoring Guide (TMG) to supplement the development of seasonal adjustment factor groupings (SAFGs), the relationships among SAFGs' characteristics remain undiscovered, while the determination of the optimal number of clusters is an ambiguous task exposed to great subjectivity. Statistical indicators provide a mathematical solution by removing engineering judgment without taking into consideration any guidelines or other criteria, necessary for transportation planners to generate ‘practical and sensible’ groupings. The method examined in this study aims to overcome the above weaknesses incorporating into the methodology a series of statistics, recommendations, and previous research findings. The investigation of the relationships among (1) the within-group variation, (2) the total number of sites, (3) the minimum number of stations within a cluster, (4) the optimal number of clusters, and (5) the geographical size of the groups constitutes the main objectives of this research. According to the results, the cluster variability declines as the available number of stations increases. When the minimum number of stations within a cluster increases, the weighted coefficient of variation inflates as well, with the rate of increase depending on sample size. The average number of automatic traffic recorders per cluster is analogous to the sample size, while the optimal number of clusters varies conversely with the minimum number of stations within a cluster. The application developed for the conduct of the analysis minimizes the computational time needed, while it can be easily implemented by engineers to automate the process recommended by the TMG, enhancing the current state of practice.

Use of Support Vector Machines to Assign Short-Term Counts to Seasonal Adjustment Factor Groups

The traditional method of estimating annual average daily traffic (AADT) involves several steps, with the most critical being the assignment process that involves allocating short-term counts to groups of seasonal adjustment factors (SAFs). The accuracy of AADT estimates highly depends on the assignment step, which is subject to errors stemming from human judgment. Support vector machines (SVMs), a supervised learning and statistical method, are employed to construct a series of assignment models that are compared with the traditional method and discriminant analysis (DA) models. Traffic volume data obtained from permanent traffic recorders are used to train and validate the models. The analysis is conducted with SAFs calculated for each direction of travel and separately for two-way traffic. The results reveal that the Gaussian kernel-based SVM model yields the lowest errors, improving AADT accuracy by 65% and decreasing the standard deviation of absolute percentage error by 73.7% over the traditional method. Another finding is that the assignment errors of the directional volume-based analysis are lower than those of the total volume-based analysis by 41.8%. The comparison between the two model parameters examined—the average daily traffic and the hourly factors—indicates that the combined use of both parameters in SVMs is more effective than when hourly factors are used alone. However, in the case of DA, the opposite results are obtained. A possible explanation is that the SVM kernels transfer data from the input space to a feature space and thus provide the ability to assign counts effectively with different types of data within the same model.

Seasonal Effects on a Low-Volume Road Flexible Pavement

Alaska's Parks Highway is a 323-mi, asphalt-paved, predominantly two-lane, low-volume roadway that links the state's two largest cities (Anchorage and Fairbanks). The effects of thawing on this low-volume roadway pavement were assessed, and seasonal adjustment factors for the different pavement layers were developed. Falling weight deflectometer (FWD) testing was carried out weekly during springtime at three temperature data probe sites. The purpose of the FWD tests was to monitor the pavement structural conditions and moduli variations during the spring thaw and recovery periods. When the pavement began to thaw, how quickly thawing progressed, and whether the pavement returned to an equilibrium summertime state were also determined. Observations indicated that profound changes in backcalculated modulus values could occur within 1 week of thaw onset. In addition, moduli were used to develop modular ratios for the subgrade and base course layers. Results showed that while thawing, the subgrade and base course layers reached about 30% to 40% and 50% to 70%, respectively, of their summertime reference moduli. These ratios were lower than those currently used in Alaskan pavement design. Additional testing should be conducted to verify the proposed seasonal adjustment factors. Surface moduli values, calculated and plotted without the need for the pavement layer thicknesses, are useful in instantly identifying whether partial thawing has occurred.

Comparison of Clustering Methods for Road Group Identification in FHWA Traffic Monitoring Approach: Effects on AADT Estimates

Defining road groups is the first step in the Federal Highway Administration (FHWA) factor approach procedure for annual average daily traffic (AADT) estimation and is one of the main sources of errors in AADT estimates. This paper focuses on a comparative analysis of cluster analysis methods to identify road groups with similar traffic patterns according to different combinations of seasonal adjustment factors calculated for passenger vehicles and trucks. The aim is to highlight the differences among methods and input variables in the AADT estimation process, optimizing information commonly available to analysts. The analysis made use of traffic data from 50 automatic traffic recorder (ATR) sites in the Province of Venice, Italy. The estimation accuracy of the clustering methods was assessed and compared by considering the values of mean absolute percent error in AADT estimates. The performance of clustering methods was found to differ, depending on data sets and traffic patterns. Particularly significant for the accuracy of AADT estimates was the choice to use seasonal adjustment factors disaggregated by vehicle type as input variables.

Modeling Seasonal Heteroscedasticity in Vehicular Traffic Condition Series Using a Seasonal Adjustment Approach

AbstractHeteroscedasticity modeling in transportation engineering is primarily conducted in short-term traffic condition forecasting to generate time varying prediction intervals around the point forecasts through quantitatively predicting the conditional variance of traffic condition series. Until recently, the generalized autoregressive conditional heteroscedasticity (GARCH) model and the stochastic volatility model have been two major approaches adopted from the field of financial time series analysis for traffic heteroscedasticity modeling. In this paper, recognizing the pronounced seasonal pattern in traffic condition data, a simple seasonal adjustment approach is explored for modeling seasonal heteroscedasticity in traffic-flow series, and four types of seasonal adjustment factors are proposed with respect to daily or weekly patterns. Using real-world traffic-flow data collected from highway systems in the United Kingdom and the United States, the proposed seasonal adjustment approach is implemented...

Application of Seasonal Adjustment Factors to Subsequent Year Data

Seasonal adjustment factors are used by state highway agencies to convert short-term traffic counts into estimates of annual average daily traffic (AADT). These factors are typically calculated from continuous traffic counter data. The protocol for the process is described in FHWA's Traffic Monitoring Guide. This protocol specifies that temporary counts collected during a year should be adjusted by seasonal factors computed from continuous data collected during the same calendar year. The authors had suggested that, if the prior year seasonal factors could be used, AADT estimates would be available soon after temporary counts were collected. This study investigated whether the seasonal factors from sequential years could be applied without loss of statistical accuracy. No significant loss of accuracy was found in estimating AADT when prior-year seasonal adjustment factors were applied to current-year short-term counts of traffic volume. Likewise, no significant loss of accuracy was found when vehicle miles of travel were computed by using prior-year seasonal adjustment factors.

Seasonal adjustment, demography, and GDP growth

AbstractSeasonal adjustment removes the predictable seasonal variation in GDP. If there is seasonal variation in factor inputs, then seasonal adjustment factors may be correlated with those inputs. This correlation can obscure statistically the importance of those inputs for GDP. This effect is apparent for the demographic composition of the labour force for Canada. Seasonal adjustment also changes the time‐series properties of GDP. Accounting for the seasonal pattern and trend change in demography suggests that the period of reduced volatility in GDP growth for Canada, the ‘Great Moderation,’ can be traced to the changing demographic structure of the labour force.

Alternatives in assigning short‐term counts to seasonal adjustment factor groupings

SUMMARYThe precise estimation of the annual average daily traffic (AADT) is a task of significant interest for many transportation authorities and Departments of Transportation. In this study, three methods are developed to improve the assignment of short‐term counts to seasonal adjustment factor (SAF) groupings: the traditional functional classification, a discriminant analysis (DA), and a new statistical approach based on a weighted coefficient of variation (WCV). The data analyzed within this study are generated from all available continuous counters within the State of Ohio between 2002 and 2006. The analysis is conducted using SAFs that are separately calculated for the total volume and the directional specific volumes of a site. The results show that the directionally based assignment errors are statistically lower at a 95% confidence interval when compared with those generated by the total volume analysis. It is also found that the hourly time‐of‐day factors are more important in the assignment process than the average daily traffic. The directionally based WCV produces a decline in the average mean absolute percentage error (MAPE) over the roadway functional classification by 58% and in the standard deviation of the absolute error (SDAE) by 70%. On the contrary, the directionally based DA lowers the MAPE and the SDAE by 35% and 60%, respectively. Copyright © 2012 John Wiley & Sons, Ltd.

Discriminant Analysis for Assigning Short-Term Counts to Seasonal Adjustment Factor Groupings

The assignment of short-term counts to groupings of seasonal adjustment factors is the most critical step in the annual average daily traffic estimation process; this step is also extremely sensitive to error resulting from engineering judgment. In this study, discriminant analysis is examined, and several variable selection criteria are investigated to develop 12 assignment models. Continuous traffic volume data, obtained in the state of Ohio during 2005 and 2006, are used in the analysis. Seasonal adjustment factors are calculated with individual volumes of the two directions of travel as well as the total volume of a roadway segment. The results reveal that the best-performing directional volume–based model, which employs the Rao's V algorithm, produces a mean absolute error (MAE) of 4.2%, which can be compared with errors reported in previous studies. An average decline in the MAE by 58% and in the standard deviation of the absolute error by 70% is estimated over the traditional roadway functional classification. In addition, time-of-day factors are slightly more effective in identifying similar patterns of short-term counts than when they are combined with the average daily traffic. When directional-specific factors are used instead of total volume–based seasonal adjustment factors, the improvement in the average MAE is approximately 41%. This conclusion is consistent with previous research findings and may result from the division of the data set by direction essentially doubling the sample size, which in turn increases the number of assignment options for a short-term count.