Prices for United States Stock Market Index (US30) including live quotes, historical charts and news. United States Stock Market Index (US30) was last updated by Trading Economics this October 27 of 2025.

The main stock market index of United States, the US500, rose to 6849 points on October 27, 2025, gaining 0.85% from the previous session. Over the past month, the index has climbed 2.82% and is up 17.61% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on October of 2025.

Crude Oil fell to 61.43 USD/Bbl on October 27, 2025, down 0.12% from the previous day. Over the past month, Crude Oil's price has fallen 3.19%, and is down 8.83% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on October of 2025.

Dataset extracted from the post US Market Prediction|13th Oct 2025| Technicals & Economic Trends on Smart Investello.

The yield on US 30 Year Bond Yield rose to 4.61% on October 27, 2025, marking a 0.01 percentage points increase from the previous session. Over the past month, the yield has fallen by 0.09 points, though it remains 0.08 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. United States 30 Year Bond Yield - values, historical data, forecasts and news - updated on October of 2025.

Graph and download economic data for 30-Year Expected Inflation (EXPINF30YR) from Jan 1982 to Oct 2025 about 30-year, projection, inflation, and USA.

Prices for US 100 Tech Index including live quotes, historical charts and news. US 100 Tech Index was last updated by Trading Economics this October 27 of 2025.

Forecast: Import of Weighing Machinery Having a Capacity of 30-5000 Kg to the US 2024 - 2028 Discover more data with ReportLinker!

Forecast: Number of Road Fatalities (After 30 Days) in the US 2024 - 2028 Discover more data with ReportLinker!

The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically from -12 meters (m) to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s). Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of current sea-level forecasts, vertical land movement rates, and current elevation data. Coastal response type predictions incorporate adjusted elevation predictions with land cover data and expert knowledge to determine the likelihood that an area will be able to accommodate or adapt to water level increases and maintain its initial land class state or transition to a new non-submerged state (dynamic) or become submerged (static). Intended users of these data include scientific researchers, coastal planners, and natural resource management communities.

[NOTE - 11/24/2021: this dataset supersedes an earlier version https://doi.org/10.15482/USDA.ADC/1518654 ] Data sources. Time series data on cattle fever tick incidence, 1959-2020, and climate variables January 1950 through December 2020, form the core information in this analysis. All variables are monthly averages or sums over the fiscal year, October 01 (of the prior calendar year, y-1) through September 30 of the current calendar year (y). Annual records on monthly new detections of Rhipicephalus microplus and R. annulatus (cattle fever tick, CFT) on premises within the Permanent Quarantine Zone (PQZ) were obtained from the Cattle Fever Tick Eradication Program (CFTEP) maintained jointly by the United States Department of Agriculture (USDA), Animal Plant Health Inspection Service and the USDA Animal Research Service in Laredo, Texas. Details of tick survey procedures, CFTEP program goals and history, and the geographic extent of the PQZ are in the main text, and in the Supporting Information (SI) of the associated paper. Data sources on oceanic indicators, on local meteorology, and their pretreatment are detailed in SI. Data pretreatment. To address the low signal-to-noise ratio and non-independence of observations common in time series, we transformed all explanatory and response variables by using a series of six consecutive steps: (i) First differences (year y minus year y-1) were calculated, (ii) these were then converted to z scores (z = (x- μ) / σ, where x is the raw value, μ is the population mean, σ is the standard deviation of the population), (iii) linear regression was applied to remove any directional trends, (iv) moving averages (typically 11-year point-centered moving averages) were calculated for each variable, (v) a lag was applied if/when deemed necessary, and (vi) statistics calculated (r, n, df, P<, p<). Principal component analysis (PCA). A matrix of z-score first differences of the 13 climate variables, and CFT (1960-2020), was entered into XLSTAT principal components analysis routine; we used Pearson correlation of the 14 x 60 matrix, and Varimax rotation of the first two components. Autoregressive Integrated Moving Average (ARIMA). An ARIMA (2,0,0) model was selected among 7 test models in which the p, d, and q terms were varied, and selection made on the basis of lowest RMSE and AIC statistics, and reduction of partial autocorrelation outcomes. A best model linear regression of CFT values on ARIMA-predicted CFT was developed using XLSTAT linear regression software with the objective of examining statistical properties (r, n, df, P<, p<), including the Durbin-Watson index of order-1 autocorrelation, and Cook’s Di distance index. Cross-validation of the model was made by withholding the last 30, and then the first 30 observations in a pair of regressions. Forecast of the next major CFT outbreak. It is generally recognized that the onset year of the first major CFT outbreak was not 1959, but may have occurred earlier in the decade. We postulated the actual underlying pattern is fully 44 years from the start to the end of a CFT cycle linked to external climatic drivers. (SI Appendix, Hypothesis on CFT cycles). The hypothetical reconstruction was projected one full CFT cycle into the future. To substantiate the projected trend, we generated a power spectrum analysis based on 1-year values of the 1959-2020 CFT dataset using SYSTAT AutoSignal software. The outcome included a forecast to 2100; this was compared to the hypothetical reconstruction and projection. Any differences were noted, and the start and end dates of the next major CFT outbreak identified. Resources in this dataset: Resource Title: CFT and climate data. File Name: climate-cft-data2.csv Resource Description: Main dataset; see data dictionary for information on each column Resource Title: Data dictionary (metadata). File Name: climate-cft-metadata2.csv Resource Description: Information on variables and their origin Resource Title: fitted models. File Name: climate-cft-models2.xlsx Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel; XLSTAT,url: https://www.xlstat.com/en/; SYStat Autosignal,url: https://www.systat.com/products/AutoSignal/

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Modular Data Centers Market Size 2024-2028

The global modular data centers market size is forecast to increase by USD 42.56 billion, at a CAGR of 19.8% between 2023 and 2028. The need to streamline traditional data centers is a major factor fueling market growth. Today, companies running single conventional data centers grapple with complex management and soaring capital costs due to sophisticated power and cooling systems. With the current economic recession, businesses are increasingly seeking cost-effective and scalable solutions. Modular data centers, with their standardized, portable designs, provide an ideal alternative that can be quickly deployed. Mobile network operators and colocation providers are among the leading users of these solutions. These modular setups are more environmentally friendly, thanks to their energy-efficient HVAC systems and IT equipment. As big data, AI, cloud computing, 5G, and IoT applications require higher operating temperatures, the flexibility and scalability of modular designs become even more crucial.

What will be the Size of the Market During the Forecast Period?

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Market Segmentation

By End-user

IT and Telecom is the Leading Segment to Dominate the Market

The IT and telecom segment is estimated to witness significant growth during the forecast period. In the global market, Modular Data Centers hold a significant share, particularly in the IT and telecom sector. These centers are essential for providing the required computing power and storage for various applications and services in the industry. With the rise of cloud computing, the demand for data centers has escalated, as businesses seek to access resources without substantial capital expenditure. The IT and telecom segment was the largest and was valued at USD 4.02 billion in 2018. The influx of data from businesses and individuals necessitates data centers capable of handling vast amounts of information. Recession or not, Modular Data Centers offer scalability and rapid deployment, making them attractive to mobile network providers and data center colocation providers. Green data centers, with their standard design and cooling systems, are increasingly popular due to their energy efficiency. Big data, AI, cloud computing, 5G infrastructure, Internet of things, and cloud-based solutions are driving the market's growth.

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North America Holds a Prominent Position in the Market

North America is estimated to contribute 30% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. The Edge computing trend is driving the growth of the market in the US and Canada, particularly in the BFSI industry. Large enterprises are shifting towards energy-efficient data centers to minimize costs and CAPEX, opting for cloud solutions from hyperscale providers like AWS, Microsoft, and Oracle. As of 2021, the US hosts over 2,670 data centers, making it the global leader. Quicksilver Capital and the World Economic Forum highlight the importance of digital transformation in this context. These offer Scalable data centers for large enterprises, enabling them to meet their computing capacity requirements efficiently.

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Market Dynamics and Customer Landscape

They have emerged as a popular solution for businesses seeking scalability and rapid deployment during times of economic uncertainty, such as a recession. These data centers utilize a modular design, allowing for easy expansion and contraction based on demand. Green data centers, which prioritize energy efficiency, are a key focus in the modular data center market. Mobile network providers and large enterprises are major consumers, as they require cloud-based networking and 5G infrastructure to support digital transformation initiatives. The solutions sub-segment and services segment of the modular data center market are expected to grow significantly, as businesses increasingly turn to cloud-based solutions for their data storage and processing needs. The World Economic Forum has the importance of energy-efficient data centers in reducing carbon emissions and mitigating the environmental impact of digitalization. Quicksilver Capital and other investors have shown interest in the modular data center market, recognizing its potential for innovation and growth. Overall, the modular data center market is poised for expansion, driven by the need for scalable, energy-efficient, and quickly deployable solutions.

Key Market Driver

Requirement to reduce complexity of traditional data centers is notably driving market growth. In today's business landscape, enterprises operating a single traditional data center face increasing complexi

Forecasting weather has become commonplace, but as society faces novel and uncertain environmental conditions there is a critical need to forecast ecology. Forewarning of ecosystem conditions during climate extremes can support proactive decision-making, yet applications of ecological forecasts are still limited. We showcase the capacity for existing marine management tools to transition to a forecasting configuration and provide skilful ecological forecasts up to 12 months in advance. The management tools use ocean temperature anomalies to help mitigate whale entanglements and sea turtle bycatch, and we show that forecasts can forewarn of human-wildlife interactions caused by unprecedented climate extremes. We further show that regionally downscaled forecasts are not a necessity for ecological forecasting and can be less skilful than global forecasts if they have fewer ensemble members. Our results highlight capacity for ecological forecasts to be explored for regions without the infrastructure or capacity to regionally downscale, ultimately helping to improve marine resource management and climate adaptation globally. Methods Summary We configure two existing resource management tools, originally configured to use observed (historical) ocean temperatures, to a forecasting system and conduct a retrospective forecast to test their skill. We first conducted a retrospective forecast using global forecasts (73 ensemble members) across the full historically available period (1981-2020) – termed the ‘Global’ model. Global forecasts of monthly sea surface temperature were obtained from the North American Multimodel Ensemble (NMME; Table S1; https://www.cpc.ncep.noaa.gov/products/NMME/). We then compared the performance of three forecast configurations: First, we used global forecasts (73 ensemble members) across a reduced historical period (1981-2010) - termed the ‘Global Full Ensemble’. Second, we used forecasts regionally downscaled (3 ensemble members) to the CCE for the same reduced historical period (1981-2010) - termed the ‘Downscaled Ensemble’. Third, we used a reduced subset of the global forecasts (3 ensemble members) for the same reduced historical period (1981-2010) - termed the ‘Global Reduced Ensemble’.
All forecasts are compared to SST observations, extracted from a CCE regional reanalysis. This reanalysis is based on the Regional Ocean Modeling System (ROMS) and covers the west coast of the U.S. (30-48˚N, 134-115.5˚W) with 0.1 degree (~10 km) horizontal resolution and 42 terrain-following vertical levels. Case Study 1: Habitat Compression Index The Habitat Compression Index (HCI) is a regionally resolved measure of cool thermal habitat along the U.S. West Coast; the index presented here monitors surface water conditions off California (35-40°N). The HCI is used to assess the degree to which upwelling habitat (indicated by cool water) is compressed against the coast, as nutrient-rich upwelled waters attract whales seeking enhanced foraging opportunities. The HCI was calculated as the number of grid cells with SST lower than a monthly SST threshold within 150 km of the coastline. The HCI was normalized by the total number of grid cells of the 150 km domain to scale values from 0 to 1. Monthly SST thresholds are the mean monthly SST from 1981-2010 from the coast to 75 km offshore. Low HCI values represent high compression, or reduction of cool thermal habitat, and are the primary interest to resource managers tasked with mitigating whale entanglement risk. The long-term mean of the HCI is used to identify a high compression event (i.e. values below the mean. Case Study 2: TOTAL Tool The Temperature Observations to Avoid Loggerheads (TOTAL) tool monitors anomalously high SST in the Southern California Bight (31-34°N, 120-116°W) as an indicator of turtle bycatch risk and to recommend potential implementation of a fishery closure. TOTAL was calculated as the six-month rolling mean of SST anomalies in the Southern California Bight domain. The spatial closure is potentially enacted during three months of the year (June, July, August) based on SSTA of the preceding six months. If SSTA exceeds a threshold, calculated as the minimum monthly anomaly value preceding three historical closure periods (Aug 2014, Jun-Aug 2015, & Jun-Aug 2016), a closure is recommended. Skill assessment Forecast skill of each management tool was assessed by comparing observed and forecast values using three metrics: (1) correlation coefficient, which provides a statistical measure of the strength of a linear relationship between observed and forecast values; (2) forecast accuracy, which indicates the fraction of correct forecasts; and (3) the Symmetric Extremal Dependence Index (SEDI) which has several properties that make it well suited to quantifying skill for rare events. Details and equations for metrics are described in the manuscript.

The 10-year treasury constant maturity rate in the U.S. is forecast to increase by 0.1 percentage points by 2027, while the 30-year fixed mortgage rate is expected to fall by 0.2 percentage points. From 6.6 percent in 2024, the average 30-year mortgage rate is projected to reach 6.4 percent in 2027.

The Gross Domestic Product (GDP) in the United States expanded 3.80 percent in the second quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - United States GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The main stock market index of United States, the US500, rose to 6792 points on October 24, 2025, gaining 0.79% from the previous session. Over the past month, the index has climbed 2.83% and is up 16.93% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on October of 2025.

We report average expected inflation rates over the next one through 30 years. Our estimates of expected inflation rates are calculated using a Federal Reserve Bank of Cleveland model that combines financial data and survey-based measures. Released monthly.

Forecast: Import of Shuttle Type Looms for Weaving Fabric of a Width Exceeding 30 cm to the US 2024 - 2028 Discover more data with ReportLinker!

The US Coffee Market Report is Segmented by Product Type (Whole Bean, Ground Coffee, Instant Coffee, and Coffee Pods and Capsules), Type (Conventional and Specialty), Packaging Type (Flexible, Rigid, and Single-Serve), Distribution Channel (On-Trade and Off-Trade Channel) and Geography (California, Texas, Florida, and More). The Market Forecasts are Provided in Terms of Value (USD).

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Video Analytics Market Size 2024-2028

The video analytics market size is valued to increase by USD 37.49 billion, at a CAGR of 33.01% from 2023 to 2028. Enhanced decision-making capabilities of users will drive the video analytics market.

Market Insights

APAC dominated the market and accounted for a 41% growth during the 2024-2028.
By End-user - Government sector segment was valued at USD 1.25 billion in 2022
By Deployment - On-premises segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 966.67 million 
Market Future Opportunities 2023: USD 37492.50 million
CAGR from 2023 to 2028 : 33.01%

Market Summary

The market is characterized by the increasing adoption of advanced technologies, such as artificial intelligence (AI) and machine learning (ML), to derive valuable insights from video data. This trend is driven by the growing preferences for IP cameras over analog cameras due to their superior image quality, flexibility, and remote access capabilities. Additionally, the need for supply chain optimization, compliance, and operational efficiency has led businesses to invest in video analytics solutions. However, the market faces challenges, including the shortage of skilled workers to manage and analyze the vast amounts of data generated by these systems. Despite these hurdles, video analytics continues to gain traction as it offers enhanced decision-making capabilities for various industries, including retail, transportation, and public safety. For instance, a retailer can use video analytics to monitor customer behavior and optimize store layouts, while a transportation company can leverage it to improve fleet management and ensure safety regulations are met. Overall, the market is poised for significant growth as businesses continue to seek ways to gain valuable insights from their video data.

What will be the size of the Video Analytics Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, with advanced technologies such as parallel video processing, low-latency streaming, and real-time insights delivery becoming increasingly essential for businesses. According to recent research, high-resolution video analytics reporting tools are experiencing significant adoption, with companies seeing up to a 30% increase in operational efficiency due to faster data processing and analysis. This trend is particularly relevant for boardroom-level decision-making areas, such as compliance and budgeting. Video format conversion, video encoding formats, and video streaming protocols are also crucial elements of the video analytics landscape. Scalable video coding, gpu accelerated processing, and video data encryption are essential for managing bandwidth and ensuring data privacy and security. Video analytics dashboards and APIs enable real-time insights delivery, while distributed video processing and video analytics workflows optimize video codec performance and reduce latency. Moreover, video resolution scaling, frame rate analysis, and video quality metrics are vital for maintaining high-quality video streaming and ensuring optimal user experience. Latency reduction techniques and video bitrate control further enhance the efficiency of video analytics systems. Video data visualization tools offer valuable insights into complex video data, making it easier for businesses to make informed decisions based on real-time data. Overall, the market is poised for continued growth, with businesses increasingly recognizing the value of leveraging video data for strategic decision-making.

Unpacking the Video Analytics Market Landscape

In the dynamic business landscape, video analytics has emerged as a critical tool for gaining valuable insights from visual data. Leveraging advanced technologies such as action recognition algorithms, object detection APIs, and video data compression, businesses can optimize operations and enhance decision-making. For instance, video data annotation using computer vision libraries leads to a 30% increase in customer behavior analytics accuracy, resulting in improved ROI. Moreover, anomaly detection systems utilizing machine learning pipelines can reduce operational costs by 25% through early identification and resolution of issues. Video content moderation, powered by sentiment analysis tools, ensures compliance with industry regulations, mitigating potential risks. With cloud-based video processing and real-time video event detection, businesses can respond swiftly to market trends and customer needs. Edge video analytics and visual search technology further expand the scope of video analytics, offering enhanced capabilities for businesses to stay competitive.

Key Market Drivers Fueling Growth

The market is driven primarily by the enhanced d