Latvia - Households without access to internet at home, because the equipment costs are too high was 24.76% in December of 2019, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Latvia - Households without access to internet at home, because the equipment costs are too high - last updated from the EUROSTAT on June of 2025. Historically, Latvia - Households without access to internet at home, because the equipment costs are too high reached a record high of 59.63% in December of 2011 and a record low of 24.76% in December of 2019.

Luxembourg - Households without access to internet at home, because the equipment costs are too high was 10.84% in December of 2019, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Luxembourg - Households without access to internet at home, because the equipment costs are too high - last updated from the EUROSTAT on June of 2025. Historically, Luxembourg - Households without access to internet at home, because the equipment costs are too high reached a record high of 11.00% in December of 2015 and a record low of 2.68% in December of 2013.

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Quad Small Form-Factor Pluggable Module Market Size 2024-2028

The quad small form-factor pluggable (QSFP) module market size is forecast to increase by USD 1.42 billion at a CAGR of 10.87% between 2023 and 2028. The market is experiencing significant growth due to the increasing demand for optical fiber communication networks and the migration of data centers towards higher bandwidth connections of 40 Gbps and 100 Gbps. With the emergence of M2M communication, IoT devices are generating massive volumes of new data. This trend is driven by the surging need for faster data transfer and processing in various industries, including telecommunications, finance, and healthcare. However, the high cost of QSFP transceiver modules remains a challenge for market growth, particularly for small and medium-sized enterprises. Despite this, the market is expected to continue expanding as technology advances and prices become more competitive. The report provides an in-depth analysis of the market growth factors, trends, and challenges, offering valuable insights for industry stakeholders.

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

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Market Dynamic and Customer Landscape

The market is witnessing rapid growth, driven by the demand for hyperscale computing and cloud-based services. QSFP modules, including QSFP28 and the emerging QSFP-DD (Double Density), are integral to high-speed data transmission and networking applications in hyperscale data centers. These modules support advanced networking technologies and interconnect solutions crucial for efficient performance in distributed computing architectures and big data analytics. With the evolution of 5G infrastructure and increasing data traffic, QSFP modules play a pivotal role in the telecommunications sector, facilitating digital transformation and enabling high-speed networks. As technological advancements continue, QSFP modules remain essential components in telecommunication applications, ensuring seamless connectivity and scalability for cloud computing environments. Our researchers analyzed the market research and growth data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

Key Market Driver

Rising optical fiber communication network connections are notably driving the market growth. Optical fibers are flexible, thin, susceptible to electromagnetic interference, and more rugged and reliable than copper cables. They are corrosion-free, present no fire hazard, and can carry signals over long distances with minimal breakdown, unlike copper cables. These characteristics of optical fiber are encouraging several industry players from different industry verticals, such as military and aerospace, mining, oil and gas, telecommunication, transportation, and utilities, to build their communication network infrastructure using fiber optics.

Furthermore, the fibre-to-the-premises (FTTP) initiatives undertaken by different countries are driving the demand for optical fiber and optical fiber components for FTTx connections. The FTTH network uses fiber optic cables instead of copper, telephone, or other cables to deliver high-speed service to the above end-user facilities. Therefore, the increased penetration of FTTH across the world is expected to drive the growth of the market during the forecast period.

Significant Market Trends

Migration to 40 Gbps and 100 Gbps data centers is a key trend influencing market growth. All this data has to be stored, managed, and retrieved between data centers located around the world. Advanced enterprise applications and server virtualization that require higher data transport rates are compelling data centers to migrate from 10 Gbps speed to 40 and 100 Gbps speed.

One way to achieve this is to deploy 40 Gbps and100 Gbps quad small form-factor pluggable (QSFP) transceiver modules that allow the reuse of the existing 10-Gbps Ethernet/optical fiber infrastructure for 40 Gbps and 100 Gbps connections. The increasing shift from 1 Gbps to 5 Gbps to support higher bandwidth access points will further drive the migration to high Gbps speed. Data centers will migrate from 10 Gbps to 100 Gbps for both enterprises and cloud providers. These factors are driving the demand for QSFP modules such as 40 Gbps and 100 Gbps QSFP modules, which, in turn, will support the market growth.

Major Market Challenge

The high cost of QSFP transceiver modules may impede market growth. Service providers are migrating to 40 Gbps and 100 Gbps speeds because of the increase in Internet IP traffic. The high average selling price of QSFP modules and the deployment of new optical equipment that is not suitable for 40/100G networks present a major cost challenge.

The price of 40 Gbps and

The median home sales price is the middle value of the prices for which homes are sold (both market and private transactions) within a calendar year. The median value is used as opposed to the average so that both extremely high and extremely low prices do not distort the prices for which homes are sold. This measure does not take into account the assessed value of a property.Source: First American Real Estate Solutions (FARES) and RBIntel (2022-forward)Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2022, 2023

Many business cycle models use a flat short-run Phillips curve, due to time-dependent pricing and strategic complementarities, to explain fluctuations in real output. But, in doing so, these models predict unrealistically high persistence and stability of US inflation in recent decades. We calculate "reset price inflation"—based on new prices chosen by the subsample of price changers—to dissect this discrepancy. We find that the models generate too much persistence and stability both in reset price inflation and in the way reset price inflation is converted into actual inflation. Our findings present a challenge to existing explanations for business cycles. (JEL E31, E52)

The average home in the U.S. sold for several percent below its asking price in December 2022, as a result of the housing market slowing. Just a few months before that, In the second quarter of 2022, the so-called sale-to-list price ratio went above 100. This reflected the high housing demand and the need of prospective home buyers to bid above the asking price. Housing demand - as measured in pending home sales - went up, as mortgage rates were historically low and plummeted once rates were increased.

Israel Rental Prices: Avg: Bnei Brak: 2.5 to 3 rooms data was reported at 4,003.700 ILS in Dec 2024. This records an increase from the previous number of 3,995.800 ILS for Sep 2024. Israel Rental Prices: Avg: Bnei Brak: 2.5 to 3 rooms data is updated quarterly, averaging 3,610.350 ILS from Mar 2017 (Median) to Dec 2024, with 32 observations. The data reached an all-time high of 4,003.700 ILS in Dec 2024 and a record low of 3,270.334 ILS in Mar 2017. Israel Rental Prices: Avg: Bnei Brak: 2.5 to 3 rooms data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.EB009: Average Rental Price: Dwellings.

Graph and download economic data for Home Price Index (High Tier) for Portland, Oregon (POXRHTNSA) from Jan 1987 to Apr 2025 about high tier, Portland, HPI, housing, price index, indexes, price, and USA.

Use our Stock prices dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Chesapeake Energy Warrants: (CHKEL) A High-Risk, High-Reward Play

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

US Tariff Impact on Market

U.S. tariffs on imported components, such as semiconductor chips, AI processors, and cloud infrastructure, have raised production costs for personal AI assistant technology providers. Many of these components are sourced from regions like Asia, where tariff increases have resulted in higher prices for the hardware necessary for AI assistants.

As a result, U.S.-based manufacturers may pass these increased costs onto consumers, potentially slowing adoption, especially among small to medium enterprises (SMEs). The impact of tariffs is particularly significant in the chatbot and customer service application segments, where scalability and efficiency are critical. U.S. tariffs are estimated to affect 10-15% of the personal AI assistant market, with cloud-based AI assistants and natural language processing technologies being the most impacted.

US Tariff Impact Percentage for Impacted Sector

The U.S. tariffs have impacted approximately 10-15% of the personal AI assistant market, particularly affecting chatbot solutions and cloud-based AI assistants that rely on imported semiconductor chips and cloud infrastructure.

Sources for US Tariff Impact Data

• Impact of Tariffs on Semiconductor and Cloud Technology: U.S. tariffs increase costs for AI hardware and cloud components.
• Cost Increases Due to Tariffs: Tariff-related price hikes for AI assistants.
• Adjustments in AI Supply Chain: U.S. companies exploring local production to mitigate tariff effects.

Economic Impact

• U.S. tariffs on critical components have raised production costs for personal AI assistants.
• Increased prices may deter adoption, particularly among cost-sensitive customers and SMEs.
• Despite these cost increases, the market remains robust due to strong demand across various sectors like customer service and entertainment.

Geographical Impact

• North America, especially the U.S., faces higher costs due to tariffs, which could slow adoption in certain applications.
• Asia-Pacific remains largely unaffected by U.S. tariffs, maintaining competitive production capabilities.
• Europe experiences moderate impacts but benefits from a diversified supply chain and reduced reliance on tariff-impacted regions.

Business Impact

• U.S. AI assistant manufacturers face higher operational costs, impacting profitability and pricing strategies.
• Price hikes could limit the adoption of personal AI assistants in smaller businesses.
• Companies are investing in local production and alternative suppliers to mitigate t...

Procurement prices of natural gas in Spain have been on a mostly stable trend throughout 2024, oscillating around ** euros per megawatt-hours. Prior to this, natural gas procurement prices in the Mediterranean country experienced a great increase between 2021 and 2022. In the latter year, the average natural gas procurement price amounted to roughly ** euros per megawatt-hours, peaking at ***** euros per megawatt-hours in September. By contrast, Spain's average procurement price of natural gas in 2020 was around ***** euros per megawatt-hours. Why are gas prices so high? One main reason behind natural gas prices soaring in the last couple of years is the post-pandemic economic recovery. As coronavirus restrictions were lifted and many industrial and commercial sectors resumed activity simultaneously, there was a sudden demand for energy. This led to a global energy supply shortage, which was further aggravated by Russia’s invasion of Ukraine in February 2022. The natural gas sector in Spain Spain has a negligible production volume of natural gas that has been on a downward trend over the past years. Meanwhile, the import volume into the Mediterranean country has seen a mostly growing tendency. Spain’s main trading partner is Algeria, which accounts for nearly one third of the overall import volume. Altogether, natural gas constitutes an important source of energy in Spain, representing over ** percent of the primary energy consumption, and coming only second to oil.

ABSTRACT Food prices play a major role in setting inflation rates, and in recent years’ global climatic conditions has worsened a lot while global demand is increasing due to the growth of the middle class in countries such as China and India. Rising food prices remains a key concern for the government of Saudi Arabia. Saudi Arabia remains vulnerable to increases in food prices due to its high dependence on imports. The Saudi economy is an open-market based economy which is reflected by data of foreign trade with trading partners of the Kingdom. High degree of economic openness of a country causes the domestic inflation rate to be affected by change in the prices of goods in the country of origin. Saudi government is facing the challenge of limiting inflation amid a spike in global food prices. Another major challenge to the effectiveness of the Saudi monetary policy is the lack of autonomy due to the pegged exchange rate system with the US dollar. This paper attempts to study the market dynamics of the kingdom of Saudi Arabia, drivers responsible for inflation and measures that has been taken by the government to deal with the situation.

Israel Rental Prices: Avg: Ashdod: 2.5 to 3 rooms data was reported at 3,791.500 ILS in Dec 2024. This records a decrease from the previous number of 3,791.900 ILS for Sep 2024. Israel Rental Prices: Avg: Ashdod: 2.5 to 3 rooms data is updated quarterly, averaging 3,354.600 ILS from Mar 2017 (Median) to Dec 2024, with 32 observations. The data reached an all-time high of 3,791.900 ILS in Sep 2024 and a record low of 2,911.856 ILS in Mar 2017. Israel Rental Prices: Avg: Ashdod: 2.5 to 3 rooms data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.EB009: Average Rental Price: Dwellings.

Bulgaria - Households without access to internet at home, because the access costs are too high (telephone, etc.) was 24.29% in December of 2019, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Bulgaria - Households without access to internet at home, because the access costs are too high (telephone, etc.) - last updated from the EUROSTAT on July of 2025. Historically, Bulgaria - Households without access to internet at home, because the access costs are too high (telephone, etc.) reached a record high of 25.37% in December of 2014 and a record low of 13.88% in December of 2010.

Meta stock price for past 10 years. Following technical indicators added.

1. Date: This column represents the date for which the data is recorded.
2. Open: The opening price of a stock on a particular trading day.
3. High: The highest price at which a stock traded during the trading day.
4. Low: The lowest price at which a stock traded during the trading day.
5. Close: The closing price of a stock on a particular trading day. This is the final price at which the stock is valued for the day.
6. Volume: The number of shares or contracts traded in a security or an entire market during a given period, usually one trading day.
7. RSI_7: 7-day Relative Strength Index. It's a momentum indicator measuring the magnitude of recent price changes to evaluate overbought or oversold conditions in the price of a stock.
8. RSI_14: 14-day Relative Strength Index. Similar to RSI_7 but calculated over 14 days.
9. CCI_7: 7-day Commodity Channel Index. It’s a technical indicator that measures the difference between the current price and the historical average price. When calculated over 7 days, it gives short-term trends.
10. CCI_14: 14-day Commodity Channel Index. Like CCI_7, but over 14 days for more medium-term trends.
11. SMA_50: 50-day Simple Moving Average. It averages the closing prices of a stock over the past 50 days.
12. EMA_50: 50-day Exponential Moving Average. Similar to SMA_50, but gives more weight to recent prices, making it more responsive to new information.
13. SMA_100: 100-day Simple Moving Average. It averages the closing prices over the past 100 days.
14. EMA_100: 100-day Exponential Moving Average. Like SMA_100, but more responsive to recent price changes.
15. MACD: Moving Average Convergence Divergence. This indicator shows the relationship between two moving averages of a stock’s price.
16. Bollinger: Bollinger Bands. A type of price envelope developed by John Bollinger.
17. TrueRange: Typically used in calculating the Average True Range (ATR), it is a measure of volatility that considers the range between the high, low, and previous close of a stock.
18. ATR_7: 7-day Average True Range. It measures market volatility by decomposing the entire range of a stock for that period.
19. ATR_14: 14-day Average True Range. Similar to ATR_7, but calculated over 14 days.

Target

Next_Day_Close: Represents the closing price of the stock for the next day. It is useful for predictive models trying to forecast future prices.

US Tariff Impact on the Market

US tariffs could have a substantial impact on the global contextual marketing market, especially in terms of cost structures and international trade dynamics. With contextual marketing relying heavily on digital platforms, mobile devices, and software solutions, tariffs on technology imports and services could result in higher operational costs for businesses.

For sectors such as activity-based marketing, which accounts for over 51.3% of the market, tariff-related increases could range between 2% and 4%, potentially leading to higher prices for end consumers. The mobile device sector, crucial for contextual delivery, may face a 3-5% rise in component costs.

Furthermore, industries like retail and consumer goods, which hold a 23.7% market share, could see reduced profit margins due to tariff-related cost increases. While tariffs may also drive companies to consider domestic alternatives to avoid additional charges, they may be faced with challenges in maintaining the competitive pricing needed in the fast-evolving digital marketing sector.

Economic Impact

• North America, the leading market, could face higher prices for technology-driven services.
• Tariffs may disrupt global supply chains, impacting regions relying on imported digital tools.
• The US market could drive the localization of digital marketing technologies to mitigate tariff effects.

Geographical Impact

• North America, the leading market, could face higher prices for technology-driven services.
• Tariffs may disrupt global supply chains, impacting regions relying on imported digital tools.
• The US market could drive the localization of digital marketing technologies to mitigate tariff effects.

Business Impact

• Higher costs could reduce the margins for businesses heavily dependent on digital marketing.
• Companies may seek alternative suppliers or in-house solutions to minimize tariff impacts.
• Small and medium-sized businesses may struggle to adapt to increased operational costs.

US Tariff Impact Percentage for Impacted Sector

The US tariffs are expected to impact sectors such as activity-based marketing (2-4%) and mobile devices (3-5%) in terms of increased costs, which could affect both pricing and competitiveness. Retail & consumer goods may experience a 1-3% rise in operational expenses due to increased import costs.

➤➤➤ Get a sample copy to discover how our research uncovers business opportunities here @ https://market.us/report/contextual-marketing-market/free-sample/

Germany DE: Price to Rent Ratio: sa data was reported at 127.280 2015=100 in 2024. This records a decrease from the previous number of 132.141 2015=100 for 2023. Germany DE: Price to Rent Ratio: sa data is updated yearly, averaging 124.182 2015=100 from Dec 1970 (Median) to 2024, with 55 observations. The data reached an all-time high of 159.163 2015=100 in 1981 and a record low of 89.430 2015=100 in 2010. Germany DE: Price to Rent Ratio: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Annual. Nominal house prices divided by rent price indices

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What happens to gold if CPI increases?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data