Graph and download economic data for Weekday-Basis Seasonal Factors: Weekdays for Calculation (G17MVSFWWKDAYS) from Jan 1996 to Jul 2026 about weekdays, weekday-basis seasonal factors, seasonal factors, and USA.

Graph and download economic data for Weekday-Basis Seasonal Factors: Auto Production (G17MVSFWAUTOS) from Jan 1996 to Jul 2026 about weekday-basis seasonal factors, seasonal factors, vehicles, production, and USA.

Automotive Production Seasonal Factor - Weekday-Basis Seasonal Factors: Weekdays for Calculation was 23.00000 Per Month in July of 2026, according to the United States Federal Reserve. Historically, Automotive Production Seasonal Factor - Weekday-Basis Seasonal Factors: Weekdays for Calculation reached a record high of 23.00000 in January of 1996 and a record low of 20.00000 in June of 1996. Trading Economics provides the current actual value, an historical data chart and related indicators for Automotive Production Seasonal Factor - Weekday-Basis Seasonal Factors: Weekdays for Calculation - last updated from the United States Federal Reserve on November of 2025.

Graph and download economic data for Regular Seasonal Factors: Light Truck Production (G17MVSFLTRUCKS) from Jan 1996 to Jul 2026 about seasonal factors, trucks, production, and USA.

Graph and download economic data for Regular Seasonal Factors: Total Truck Production (G17MVSFTTRUCKS) from Jan 1996 to Jul 2026 about seasonal factors, trucks, production, and USA.

Abstract This study aims to present an alternative technique of exponential smoothing to estimate the demand for items with intermittent and seasonal demand. The usual technique would aggregate demand periods (months in quarters, for instance) to calculate a seasonality factor for the set of periods. The estimate for the set would be divided by the number of periods comprising it to calculate the demand per period. This technique recalculates the basis and seasonality factor for each set, and provides equal estimates for all periods within the set. The alternative herein presented also recalculates seasonal factors for every set of periods, but recalculates the demand basis for each period, allowing better monitoring of demand behavior. Based on a real-life case, the results obtained by the two techniques mentioned above and by others that do not explicitly consider seasonality were compared: simple moving average, no-seasonality exponential smoothing, Croston’s, and Syntetos-Boylan. The latter two were developed specifically for intermittent demands without seasonality. The techniques that consider seasonality performed better for estimation errors. The suggested technique, in the example, showed less bias, although with somewhat lower accuracy than exponential smoothing with seasonality and period aggregation.

This article extends the methodology for multivariate seasonal adjustment by exploring the statistical modeling of seasonality jointly across multiple time series, using latent dynamic factor models fitted using maximum likelihood estimation. Signal extraction methods for the series then allow us to calculate a model-based seasonal adjustment. We emphasize several facets of our analysis: (i) we quantify the efficiency gain in multivariate signal extraction versus univariate approaches; (ii) we address the problem of the preservation of economic identities; (iii) we describe a foray into seasonal taxonomy via the device of seasonal co-integration rank. These contributions are developed through two empirical studies of aggregate U.S. retail trade series and U.S. regional housing starts. Our analysis identifies different seasonal subcomponents that are able to capture the transition from prerecession to postrecession seasonal patterns. We also address the topic of indirect seasonal adjustment by analyzing the regional aggregate series. Supplementary materials for this article are available online.

Graph and download economic data for Weekday-Basis Seasonal Factors: Total Truck Production (G17MVSFWTTRUCKS) from Jan 1996 to Jul 2026 about weekday-basis seasonal factors, seasonal factors, trucks, production, and USA.

Japan BoP: SF: SI: CR data was reported at 0.761 NA in Feb 2025. This records a decrease from the previous number of 0.871 NA for Jan 2025. Japan BoP: SF: SI: CR data is updated monthly, averaging 0.962 NA from Jan 1996 (Median) to Feb 2025, with 350 observations. The data reached an all-time high of 1.640 NA in Jun 2017 and a record low of 0.712 NA in May 2004. Japan BoP: SF: SI: CR data remains active status in CEIC and is reported by Bank of Japan. The data is categorized under Global Database’s Japan – Table JP.JB008: Balance of Payment: BPM6: Seasonal Factors.

Data from proposed journal publication, figures and tables, paper title: Seasonal Emission Factors from Rangeland Prescribed Burns in the Kansas Flint Hills Grasslands. This dataset is associated with the following publication: Aurell, J., B. Gullett, G. Grier, A. Holder, and I. George. Seasonal Emission Factors from Rangeland Prescribed Burns in the Kansas Flint Hills Grasslands. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 304: 119769, (2023).

This dataset provides detailed insights into retail sales, featuring a range of factors that influence sales performance. It includes records on sales revenue, units sold, discount percentages, marketing spend, and the impact of seasonal trends and holidays.

Key Features:

• Sales Revenue (USD): Total revenue generated from sales.
• Units Sold: Quantity of items sold.
• Discount Percentage: The percentage discount applied to products.
• Marketing Spend (USD): Budget allocated to marketing efforts.
• Store ID: Identifier for the retail store.
• Product Category: The category to which the product belongs (e.g., Electronics, Clothing).
• Date: The date when the sale occurred.
• Store Location: Geographic location of the store.
• Day of the Week: Day when the sale took place.
• Holiday Effect: Indicator of whether the sale happened during a holiday period.

Use Cases:

• Predictive Modeling: Build models to forecast future sales based on historical data.
• Marketing Analysis: Evaluate the effectiveness of marketing spend and discount strategies.
• Seasonal Trend Analysis: Examine how different seasons and holidays impact sales.
• Revenue Optimization: Identify strategies to optimize pricing and marketing for increased revenue.

Notes:

This dataset is synthetic and generated for analysis purposes. It reflects typical retail sales patterns and is designed to support a wide range of data science and business analytics projects.

The dataset includes observations made from six radio-collared elephant bulls which were observed twice per week from June 2007-June 2008 in the Kruger National Park (KNP), South Africa. These observations were made ad libitum and include the percentage time the six bulls spent foraging and resting. With foraging further divided into grazing, browsing, mixed feeding and time spent feeding others. The season (wet versus dry) these observations were made in was also noted as well as the time of day (before noon versus after noon). Finally, whether the bulls were in or out of musth was also observed as well as the associations they formed (alone, with other males only; or with females regardless of the presence of other males).

Many studies have reported obvious seasonal differences in the intestinal flora of rats, and this stable distribution of the seasonal flora helps in maintaining the normal physiological function of the host. However, the mechanism underlying these seasonal differences in intestinal flora remains unclear. To explore the correlation among seasonal factors and intestinal water metabolism and intestinal flora, 20 Sprague Dawley (SD) rats were divided into spring, summer, autumn, and winter groups. The environment for the four seasons was simulated using the Balanced Temperature and Humidity Control system. The intestinal water metabolism was evaluated by determining the intestinal transmission function, fecal water content, water content of colonic tissue, and the colonic expression levels of AQP3, AQP4, and AQP8. The composition and relative abundance of intestinal microflora in rats in each season were assessed through 16S rDNA amplifier sequencing, and the relationship between the dominant flora and intestinal water metabolism in each season was analyzed using Spearman correlation analysis. The high temperature and humidity season could lead to an increase in intestinal water metabolism and intestinal water content in rats, whereas the low temperature and humidity season could lead to a decrease, which was closely related to the change in microflora. To explore the molecular mechanism of seasonal changes in intestinal water metabolism, the concentration of colonic 5-HT, VIP, cAMP, and PKA associated with intestinal water metabolism in rats were also examined. Seasonal changes could affect the concentration of colonic 5-HT and VIP in rats, and then regulate AQPs through cAMP/PKA pathway to affect the intestinal water metabolism. These results suggest that seasonal factors affect the level of intestinal water metabolism in rats and result in seasonal differences in intestinal flora.

Seasonal Road Closure Data Feeds Market Outlook

According to our latest research, the Global Seasonal Road Closure Data Feeds market size was valued at $412 million in 2024 and is projected to reach $1.09 billion by 2033, expanding at a robust CAGR of 11.2% during the forecast period of 2025–2033. The primary factor propelling the growth of the Seasonal Road Closure Data Feeds market on a global scale is the increasing reliance on real-time and predictive data for navigation, logistics, and public safety applications. As transportation networks become more complex and susceptible to disruptions caused by weather, maintenance, and other seasonal factors, the demand for accurate and timely road closure data feeds is surging across multiple sectors. This trend is further amplified by the growing integration of advanced analytics, artificial intelligence, and IoT technologies, which enable seamless, automated data dissemination and actionable insights for end-users.

Regional Outlook

North America currently dominates the Seasonal Road Closure Data Feeds market, accounting for the largest market share, driven by the region’s mature transportation infrastructure, early adoption of intelligent transportation systems, and supportive regulatory frameworks. The United States, in particular, leads in terms of both market value and technological innovation, with federal and state agencies investing heavily in real-time data platforms and collaborative public-private data sharing initiatives. Additionally, the proliferation of navigation and mapping service providers, coupled with the widespread use of advanced telematics by logistics and fleet management companies, has solidified North America’s position as the frontrunner in this market. The presence of key industry players, robust funding for smart city projects, and stringent safety regulations further contribute to the region’s sustained leadership.

Asia Pacific is emerging as the fastest-growing region in the Seasonal Road Closure Data Feeds market, projected to register a CAGR exceeding 13.5% over the forecast period. This rapid growth is fueled by substantial investments in transportation infrastructure, especially in countries such as China, India, Japan, and South Korea, where urbanization and vehicle density are on the rise. Governments in the region are increasingly prioritizing intelligent traffic management solutions to address congestion, safety, and environmental concerns. Furthermore, the adoption of cloud-based and AI-driven data platforms is accelerating, supported by public-private partnerships and the expansion of digital mapping services. The region’s dynamic technology ecosystem and the proliferation of smartphone-based navigation apps are also major contributors to market expansion.

Emerging economies in Latin America, the Middle East, and Africa are gradually embracing Seasonal Road Closure Data Feeds, albeit at a slower pace due to certain adoption challenges. Limited digital infrastructure, inconsistent data standards, and budget constraints are significant barriers, particularly in rural and underdeveloped regions. However, localized demand is growing, especially for public safety and emergency response applications, as extreme weather events and infrastructure development projects become more frequent. Policy reforms and international cooperation are beginning to address some of these challenges, creating new opportunities for technology providers and encouraging the adoption of scalable, cost-effective data solutions tailored to regional needs.

Report Scope

Graph and download economic data for Weekday-Basis Seasonal Factors: Light Truck Production (G17MVSFWLTRUCKS) from Jan 1996 to Jul 2026 about weekday-basis seasonal factors, seasonal factors, trucks, production, and USA.

Japan BoP: SF: PI: II: Oth Investment: CR data was reported at 0.813 NA in Feb 2025. This records a decrease from the previous number of 1.025 NA for Jan 2025. Japan BoP: SF: PI: II: Oth Investment: CR data is updated monthly, averaging 0.911 NA from Jan 1996 (Median) to Feb 2025, with 350 observations. The data reached an all-time high of 1.757 NA in Sep 2011 and a record low of 0.710 NA in Feb 2017. Japan BoP: SF: PI: II: Oth Investment: CR data remains active status in CEIC and is reported by Bank of Japan. The data is categorized under Global Database’s Japan – Table JP.JB008: Balance of Payment: BPM6: Seasonal Factors.

IntroductionMajor food-importing countries are characterized by highly concentrated import sources, which easily exposes them to risks of food supply shortages due to over-reliance on a limited number of source countries.MethodsUsing China’s soybean imports as a case study, this study proposes a novel analytical framework that specifically addresses the unique characteristics of agricultural products. A multi-objective optimization model is employed to both validate the framework’s rationality and explore optimization schemes for China’s import source layout.Results and discussionThe results indicate that, first, neglecting seasonal factors in optimizing China’s soybean import source layout may increase fluctuations in soybean import quantities. The import optimization considering seasonal factors can reduce risks at equivalent costs while ensuring import stability. Second, increased soybean export availability from Russia or Kazakhstan can further reduce risks at equivalent costs while ensuring the stability of soybean imports. This study establishes an analytical framework for optimizing import layout that conforms to the unique characteristics of agricultural products, aiming to achieve sustainable food supply and ensure food security in importing countries.

To ensure respondent confidentiality, estimates below a certain threshold are suppressed. For Canada, Quebec, Ontario, Alberta and British Columbia suppression is applied to all data below 1,500. The threshold level for Newfoundland and Labrador, Nova Scotia, New Brunswick, Manitoba and Saskatchewan is 500, while in Prince Edward Island, estimates under 200 are suppressed. For census metropolitan areas (CMAs) and economic regions (ERs), use their respective provincial suppression levels mentioned above. Estimates are based on smaller sample sizes the more detailed the table becomes, which could result in lower data quality. Fluctuations in economic time series are caused by seasonal, cyclical and irregular movements. A seasonally adjusted series is one from which seasonal movements have been eliminated. Seasonal movements are defined as those which are caused by regular annual events such as climate, holidays, vacation periods and cycles related to crops, production and retail sales associated with Christmas and Easter. It should be noted that the seasonally adjusted series contain irregular as well as longer-term cyclical fluctuations. The seasonal adjustment program is a complicated computer program which differentiates between these seasonal, cyclical and irregular movements in a series over a number of years and, on the basis of past movements, estimates appropriate seasonal factors for current data. On an annual basis, the historic series of seasonally adjusted data are revised in light of the most recent information on changes in seasonality. Number of civilian, non-institutionalized persons 15 years of age and over who, during the reference week, were employed or unemployed. Estimates in thousands, rounded to the nearest hundred. Number of persons who, during the reference week, worked for pay or profit, or performed unpaid family work or had a job but were not at work due to own illness or disability, personal or family responsibilities, labour dispute, vacation, or other reason. Those persons on layoff and persons without work but who had a job to start at a definite date in the future are not considered employed. Estimates in thousands, rounded to the nearest hundred. Number of persons who, during the reference week, were without work, had looked for work in the past four weeks, and were available for work. Those persons on layoff or who had a new job to start in four weeks or less are considered unemployed. Estimates in thousands, rounded to the nearest hundred. The unemployment rate is the number of unemployed persons expressed as a percentage of the labour force. The unemployment rate for a particular group (age, gender, marital status, etc.) is the number unemployed in that group expressed as a percentage of the labour force for that group. Estimates are percentages, rounded to the nearest tenth. Industry refers to the general nature of the business carried out by the employer for whom the respondent works (main job only). Industry estimates in this table are based on the 2022 North American Industry Classification System (NAICS). Formerly Management of companies and administrative and other support services"." This combines the North American Industry Classification System (NAICS) codes 11 to 91. This combines the North American Industry Classification System (NAICS) codes 11 to 33. This combines the North American Industry Classification System (NAICS) codes 41 to 91. Unemployed persons who have never worked before, and those unemployed persons who last worked more than 1 year ago. For more information on seasonal adjustment see Seasonally adjusted data - Frequently asked questions." Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 1100 - Farming - not elsewhere classified (nec). When the type of farm activity cannot be distinguished between crop and livestock, (for example: mixed farming). Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 2100 - Mining - not elsewhere classified (nec). Whenever the type of mining activity cannot be distinguished. Also referred to as Natural resources. The standard error (SE) of an estimate is an indicator of the variability associated with this estimate, as the estimate is based on a sample rather than the entire population. The SE can be used to construct confidence intervals and calculate coefficients of variation (CVs). The confidence interval can be built by adding the SE to an estimate in order to determine the upper limit of this interval, and by subtracting the same amount from the estimate to determine the lower limit. The CV can be calculated by dividing the SE by the estimate. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of the standard errors for 12 previous months The standard error (SE) for the month-to-month change is an indicator of the variability associated with the estimate of the change between two consecutive months, because each monthly estimate is based on a sample rather than the entire population. To construct confidence intervals, the SE is added to an estimate in order to determine the upper limit of this interval, and then subtracted from the estimate to determine the lower limit. Using this method, the true value will fall within one SE of the estimate approximately 68% of the time, and within two standard errors approximately 95% of the time. For example, if the estimated employment level increases by 20,000 from one month to another and the associated SE is 29,000, the true value of the employment change has a 68% chance of falling between -9,000 and +49,000. Because such a confidence interval includes zero, the 20,000 change would not be considered statistically significant. However, if the increase is 30,000, the confidence interval would be +1,000 to +59,000, and the 30,000 increase would be considered statistically significant. (Note that 30,000 is above the SE of 29,000, and that the confidence interval does not include zero.) Similarly, if the estimated employment declines by 30,000, then the true value of the decline would fall between -59,000 and -1,000. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of standard errors for 12 previous months. They are updated twice a year The standard error (SE) for the year-over-year change is an indicator of the variability associated with the estimate of the change between a given month in a given year and the same month of the previous year, because each month's estimate is based on a sample rather than the entire population. The SE can be used to construct confidence intervals: it can be added to an estimate in order to determine the upper limit of this interval, and then subtracted from the same estimate to determine the lower limit. Using this method, the true value will fall within one SE of the estimate, approximately 68% of the time, and within two standard errors, approximately 95% of the time. For example, if the estimated employment level increases by 160,000 over 12 months and the associated SE is 55,000, the true value of the change in employment has approximately a 68% chance of falling between +105,000 and +215,000. This change would be considered statistically significant at the 68% level as the confidence interval excludes zero. However, if the increase is 40,000, the interval would be -15,000 to +95,000, and this increase would not be considered statistically significant since the interval includes zero. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of standard errors for 12 previous months and are updated twice a year Excluding the territories. Starting in 2006, enhancements to the Labour Force Survey data processing system may have introduced a level shift in some estimates, particularly for less common labour force characteristics. Use caution when comparing estimates before and after 2006. For more information, contact statcan.labour-travail.statcan@statcan.gc.ca

Giant Cell Arteritis (GCA) incidence is influenced by various unexplained factors. Over the past 60 years, studies have investigated seasonal influence on GCA incidence, yielding contradictory results. This systematic review and meta-analysis aim to determine whether there is a pooled seasonal influence on GCA incidence and if it is modulated by latitude. PubMed and Scopus databases were searched for studies on seasonal influence on GCA incidence without date restriction. Articles reporting incidence and describing seasonal or monthly proportions of GCA were considered for inclusion. The primary outcome measured was the Seasonal Incidence Risk Ratio (SIRR) defined as the incidence of GCA in warm seasons (Spring and Summer) over GCA incidence in cold seasons (Autumn and Winter). Meta-analysis of GCA incidence variations with season was performed on the pooled SIRR. Nineteen articles describing GCA incidence and seasonal variations in 39829 patients were included, 10 studies reported a significant seasonal pattern in the incidence of GCA with seven studies reporting a warm seasonal pattern and three studies reporting a cold seasonal while 9 studies did not report a significant seasonal pattern. The pooled SIRR estimate in this meta-analysis was 1.08 (95% CI [0.99-1.17]). We observed a significant reverse correlation between SIRR and the studies’ location latitude r= -0.595 (p=0.015), additionally, we observed an inflexion latitude line in Lyon, France (45.8), all studies performed southern to that line reported higher proportion of warm seasons cases. The pooled SIRR of studies performed southern to the inflexion line was 1.18 (95% CI [1.09-1.28]). this meta-analysis shows a global pooled trend towards a warm seasonal pattern, mainly driven by southern locations of the included studies, linking two largely discussed environmental factors in GCA, in this case, latitude and seasonal influence which should encourage epidemiological research in southern regions of the globe in order to display globally representative data.

BackgroundWe assessed the severity of the 2009 influenza pandemic by comparing pandemic mortality to seasonal influenza mortality. However, reported pandemic deaths were laboratory-confirmed – and thus an underestimation – whereas seasonal influenza mortality is often more inclusively estimated. For a valid comparison, our study used the same statistical methodology and data types to estimate pandemic and seasonal influenza mortality. Methods and FindingsWe used data on all-cause mortality (1999–2010, 100% coverage, 16.5 million Dutch population) and influenza-like-illness (ILI) incidence (0.8% coverage). Data was aggregated by week and age category. Using generalized estimating equation regression models, we attributed mortality to influenza by associating mortality with ILI-incidence, while adjusting for annual shifts in association. We also adjusted for respiratory syncytial virus, hot/cold weather, other seasonal factors and autocorrelation. For the 2009 pandemic season, we estimated 612 (range 266–958) influenza-attributed deaths; for seasonal influenza 1,956 (range 0–3,990). 15,845 years-of-life-lost were estimated for the pandemic; for an average seasonal epidemic 17,908. For 0–4 yrs of age the number of influenza-attributed deaths during the pandemic were higher than in any seasonal epidemic; 77 deaths (range 61–93) compared to 16 deaths (range 0–45). The ≥75 yrs of age showed a far below average number of deaths. Using pneumonia/influenza and respiratory/cardiovascular instead of all-cause deaths consistently resulted in relatively low total pandemic mortality, combined with high impact in the youngest age category. ConclusionThe pandemic had an overall moderate impact on mortality compared to 10 preceding seasonal epidemics, with higher mortality in young children and low mortality in the elderly. This resulted in a total number of pandemic deaths far below the average for seasonal influenza, and a total number of years-of-life-lost somewhat below average. Comparing pandemic and seasonal influenza mortality as in our study will help assessing the worldwide impact of the 2009 pandemic.