Graph and download economic data for Research Consumer Price Index: All Items (CPIEALL) from Dec 1982 to Aug 2025 about 62 and older, consumer prices, all items, consumer, CPI, inflation, price index, indexes, price, and USA.

Brazil National Consumer Price Index (CPI): IPCA-E data was reported at 7,097.200 Dec1993=100 in Mar 2025. This records an increase from the previous number of 7,052.070 Dec1993=100 for Feb 2025. Brazil National Consumer Price Index (CPI): IPCA-E data is updated monthly, averaging 2,815.600 Dec1993=100 from Jan 1992 (Median) to Mar 2025, with 395 observations. The data reached an all-time high of 7,097.200 Dec1993=100 in Mar 2025 and a record low of 0.418 Dec1993=100 in Jan 1992. Brazil National Consumer Price Index (CPI): IPCA-E data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.IB052: Consumer Price Index: Special Broad Category - IPCA-E: POF 2008-2009: Dec 1993=100. IBGE published a historical series of price indices of IPCA-E kept with the same base year December 1993 = 100 with five different weighting structures over the entire period. From Nov 1991 to July 1999 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey - Pesquisa de Orçamentos Familiares (POF) 1987-1988. From August 1999 to June 2006 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 1995-1996. From July 2006 to Jan 2011 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 2002-2003. From Feb 2012 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 2008-2009.

Os dados de CPI: mudança de alimentos e bebidas não alcoólicas dos Estados Unidos foram registrados em 2.519 % em 2025-02. Este é um registro de um aumento com relação aos números anteriores de 2.408 % em 2025-01. Os dados de CPI: mudança de alimentos e bebidas não alcoólicas dos Estados Unidos são atualizados por mês, com uma média de 3.099 % em 1968-01 até 2025-02, com 686 observações. Os dados alcançaram um alto recorde de 18.681 % em 1974-02 e um baixo recorde de -0.467 % em 2009-11. Os dados de CPI: mudança de alimentos e bebidas não alcoólicas dos Estados Unidos permanecem com status ativo na CEIC e são reportados pela fonte: CEIC Data. Os dados são classificados sob o World Trend Plus’ Global Economic Monitor – Table: CPI: Food and Non Alcoholic Beverage: Y-o-Y Growth: Monthly: Seasonally Adjusted.

Brazil National Consumer Price Index (CPI): IPCA-E: Food Products & Beverage data was reported at 8,280.680 Dec1993=100 in Mar 2025. This records an increase from the previous number of 8,191.390 Dec1993=100 for Feb 2025. Brazil National Consumer Price Index (CPI): IPCA-E: Food Products & Beverage data is updated monthly, averaging 2,562.090 Dec1993=100 from Jan 1992 (Median) to Mar 2025, with 395 observations. The data reached an all-time high of 8,280.680 Dec1993=100 in Mar 2025 and a record low of 0.410 Dec1993=100 in Jan 1992. Brazil National Consumer Price Index (CPI): IPCA-E: Food Products & Beverage data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.IB052: Consumer Price Index: Special Broad Category - IPCA-E: POF 2008-2009: Dec 1993=100. IBGE published a historical series of price indices of IPCA-E kept with the same base year December 1993 = 100 with five different weighting structures over the entire period. From Nov 1991 to July 1999 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey - Pesquisa de Orçamentos Familiares (POF) 1987-1988. From August 1999 to June 2006 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 1995-1996. From July 2006 to Jan 2011 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 2002-2003. From Feb 2012 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 2008-2009.

L'indice dei prezzi al consumo CPI negli Stati Uniti è aumentato a 323,98 punti ad agosto dai 323,05 punti di luglio del 2025. Questa pagina fornisce il valore più recente riportato per l'Indice dei prezzi al consumo (CPI) degli Stati Uniti, oltre alle pubblicazioni precedenti, massimi e minimi storici, previsioni a breve termine e previsioni a lungo termine, calendario economico, consenso degli esperti e notizie.

Germany Consumer Price Index (CPI): Weights: RC: AE: RT: Headphones, E-Readers, etc data was reported at 3.700 Per 1000 in 2023. This stayed constant from the previous number of 3.700 Per 1000 for 2022. Germany Consumer Price Index (CPI): Weights: RC: AE: RT: Headphones, E-Readers, etc data is updated yearly, averaging 3.700 Per 1000 from Dec 2020 (Median) to 2023, with 4 observations. The data reached an all-time high of 3.700 Per 1000 in 2023 and a record low of 3.700 Per 1000 in 2023. Germany Consumer Price Index (CPI): Weights: RC: AE: RT: Headphones, E-Readers, etc data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.I032: Consumer Price Index: Weights: Annual.

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

Os dados de CPI: mudança de alimentos e bebidas não alcoólicas do México foram registrados em 3.627 % em 2025-02. Este é um registro de um aumento com relação aos números anteriores de 2.385 % em 2025-01. Os dados de CPI: mudança de alimentos e bebidas não alcoólicas do México são atualizados por mês, com uma média de 9.010 % em 1970-01 até 2025-02, com 662 observações. Os dados alcançaram um alto recorde de 175.171 % em 1988-01 e um baixo recorde de 0.316 % em 2006-05. Os dados de CPI: mudança de alimentos e bebidas não alcoólicas do México permanecem com status ativo na CEIC e são reportados pela fonte: CEIC Data. Os dados são classificados sob o World Trend Plus’ Global Economic Monitor – Table: CPI: Food and Non Alcoholic Beverage: Y-o-Y Growth: Monthly.

Malaysia Consumer Price Index (CPI): E: TE: Tertiary Education data was reported at 114.700 2010=100 in Mar 2025. This stayed constant from the previous number of 114.700 2010=100 for Feb 2025. Malaysia Consumer Price Index (CPI): E: TE: Tertiary Education data is updated monthly, averaging 111.000 2010=100 from Jan 2010 (Median) to Mar 2025, with 183 observations. The data reached an all-time high of 115.600 2010=100 in Mar 2024 and a record low of 98.600 2010=100 in Feb 2010. Malaysia Consumer Price Index (CPI): E: TE: Tertiary Education data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.I001: Consumer Price Index: 2010=100.

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 is cpi?

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

Single SNP regression analysis of CPI.

CPI Film Market Outlook

The global CPI (Colorless Polyimide) film market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 3.3 billion by 2032, exhibiting a robust compound annual growth rate (CAGR) of 11.4% during the forecast period. This impressive growth trajectory is primarily driven by the increasing demand for flexible electronic devices and advancements in material science that enhance the properties of CPI films, making them suitable for a wide range of high-performance applications. As industries across various sectors strive for innovative solutions that offer both flexibility and durability, CPI films are becoming an integral component, driving market expansion globally.

The surging demand for CPI films is largely attributed to their exceptional properties, such as high thermal stability, transparency, and flexibility, which are crucial for next-generation electronic devices. The electronics industry, in particular, is witnessing a paradigm shift towards flexible displays and printed circuit boards, which heavily rely on the unique attributes of CPI films. As consumer electronics evolve to offer more versatile and durable products, the need for materials that can withstand rigorous operational environments without compromising on performance continues to rise, propelling the CPI film market forward.

Moreover, the drive towards energy-efficient and environmentally sustainable solutions is boosting the CPI film market. Flexible solar cells, for instance, represent a significant application where CPI films offer substantial benefits by enhancing the durability and efficiency of photovoltaic panels. The lightweight and flexible nature of these films make them ideal for integration into innovative energy solutions, expanding their applicability and enhancing their market appeal. As global energy demands increase and environmental regulations become more stringent, CPI films offer a viable solution that aligns with the objectives of sustainability and efficiency.

The automotive and aerospace industries also contribute significantly to the growth of the CPI film market. In automotive, CPI films are increasingly used in dashboards and sensor applications, owing to their ability to withstand extreme temperatures and mechanical stress. Meanwhile, the aerospace sector benefits from the lightweight yet robust properties of CPI films, which are utilized in various components to improve fuel efficiency and reduce overall weight. As these industries continue to innovate and expand their technological capabilities, the reliance on advanced materials like CPI films is expected to grow, further supporting market expansion.

Regionally, Asia Pacific stands as the dominant force in the CPI film market, driven by a robust manufacturing sector and significant investments in electronics and semiconductor industries. The region's leadership in technology innovation, coupled with a strong supply chain infrastructure, provides a fertile ground for market growth. North America and Europe are also key players, benefiting from high R&D investments and a focus on advanced material applications. These regions continue to adopt CPI films in various high-tech applications, contributing to a balanced global market outlook with growth opportunities spread across multiple geographies.

Product Type Analysis

The CPI film market, segmented by product type, includes Transparent CPI Film, Black CPI Film, and Others. Transparent CPI films dominate this segment due to their wide applicability in electronics, particularly in flexible displays. These films offer excellent optical clarity and flexibility, making them ideal for use in next-generation screens and other electronic applications that demand both durability and high-quality visual performance. As the market for flexible electronics grows, the demand for transparent CPI films is expected to rise significantly, driving innovation and production capacities in this segment.

Black CPI films, on the other hand, cater to specific applications where light absorption or opacity is required, such as in certain automotive and aerospace components. These films are valued for their enhanced thermal and mechanical properties, which allow them to function effectively in environments with extreme conditions. Although the market size for black CPI films is relatively smaller compared to transparent films, they hold a crucial position in specialized applications where performance is paramount. The ongoing advancements in formulation and processing technologies are likely to expand their applicability and marke

Brazil National Consumer Price Index (CPI): IPCA-E: Apparel data was reported at 3,785.530 Dec1993=100 in Mar 2025. This records an increase from the previous number of 3,774.960 Dec1993=100 for Feb 2025. Brazil National Consumer Price Index (CPI): IPCA-E: Apparel data is updated monthly, averaging 1,697.610 Dec1993=100 from Jan 1992 (Median) to Mar 2025, with 395 observations. The data reached an all-time high of 3,785.530 Dec1993=100 in Mar 2025 and a record low of 0.418 Dec1993=100 in Jan 1992. Brazil National Consumer Price Index (CPI): IPCA-E: Apparel data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.IB052: Consumer Price Index: Special Broad Category - IPCA-E: POF 2008-2009: Dec 1993=100. IBGE published a historical series of price indices of IPCA-E kept with the same base year December 1993 = 100 with five different weighting structures over the entire period. From Nov 1991 to July 1999 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey - Pesquisa de Orçamentos Familiares (POF) 1987-1988. From August 1999 to June 2006 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 1995-1996. From July 2006 to Jan 2011 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 2002-2003. From Feb 2012 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 2008-2009.

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.

LON:CPI CAPITA PLC

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

Prostaglandins play a critical physiological role in both cardiovascular and immune systems, acting through their interactions with 9 prostanoid G protein-coupled receptors (GPCRs). These receptors are important therapeutic targets for a variety of diseases including arthritis, allergies, type 2 diabetes, and cancer. The DP prostaglandin receptor is of interest because it has unique structural and physiological properties. Most notably, DP does not have the 3–6 ionic lock common to Class A GPCRs. However, the lack of X-ray structures for any of the 9 prostaglandin GPCRs hampers the application of structure-based drug design methods to develop more selective and active medications to specific receptors. We predict here 3D structures for the DP prostaglandin GPCR, based on the GEnSeMBLE complete sampling with hierarchical scoring (CS-HS) methodology. This involves evaluating the energy of 13 trillion packings to finally select the best 20 that are stable enough to be relevant for binding to antagonists, agonists, and modulators. To validate the predicted structures, we predict the binding site for the Merck cyclopentanoindole (CPI) selective antagonist docked to DP. We find that the CPI binds vertically in the 1–2–7 binding pocket, interacting favorably with residues R3107.40 and K762.54 with additional interactions with S3137.43, S3167.46, S191.35, etc. This binding site differs significantly from that of antagonists to known Class A GPCRs where the ligand binds in the 3–4–5–6 region. We find that the predicted binding site leads to reasonable agreement with experimental Structure–Activity Relationship (SAR). We suggest additional mutation experiments including K762.54, E1293.49, L1233.43, M2706.40, F2746.44 to further validate the structure, function, and activation mechanism of receptors in the prostaglandin family. Our structures and binding sites are largely consistent and improve upon the predictions by Li et al. (J. Am. Chem. Soc. 2007, 129 (35), 10720) that used our earlier MembStruk prediction methodology.

Brazil National Consumer Price Index (CPI): IPCA-E: Household Articles data was reported at 2,866.560 Dec1993=100 in Mar 2025. This records an increase from the previous number of 2,865.700 Dec1993=100 for Feb 2025. Brazil National Consumer Price Index (CPI): IPCA-E: Household Articles data is updated monthly, averaging 1,699.240 Dec1993=100 from Jan 1992 (Median) to Mar 2025, with 395 observations. The data reached an all-time high of 2,866.560 Dec1993=100 in Mar 2025 and a record low of 0.423 Dec1993=100 in Jan 1992. Brazil National Consumer Price Index (CPI): IPCA-E: Household Articles data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.IB052: Consumer Price Index: Special Broad Category - IPCA-E: POF 2008-2009: Dec 1993=100. IBGE published a historical series of price indices of IPCA-E kept with the same base year December 1993 = 100 with five different weighting structures over the entire period. From Nov 1991 to July 1999 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey - Pesquisa de Orçamentos Familiares (POF) 1987-1988. From August 1999 to June 2006 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 1995-1996. From July 2006 to Jan 2011 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 2002-2003. From Feb 2012 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 2008-2009.

Brazil National Consumer Price Index (CPI): IPCA-E: Housing data was reported at 11,871.310 Dec1993=100 in Mar 2025. This records an increase from the previous number of 11,827.550 Dec1993=100 for Feb 2025. Brazil National Consumer Price Index (CPI): IPCA-E: Housing data is updated monthly, averaging 4,474.210 Dec1993=100 from Jan 1992 (Median) to Mar 2025, with 395 observations. The data reached an all-time high of 11,895.230 Dec1993=100 in Nov 2024 and a record low of 0.480 Dec1993=100 in Jan 1992. Brazil National Consumer Price Index (CPI): IPCA-E: Housing data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.IB052: Consumer Price Index: Special Broad Category - IPCA-E: POF 2008-2009: Dec 1993=100. IBGE published a historical series of price indices of IPCA-E kept with the same base year December 1993 = 100 with five different weighting structures over the entire period. From Nov 1991 to July 1999 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey - Pesquisa de Orçamentos Familiares (POF) 1987-1988. From August 1999 to June 2006 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 1995-1996. From July 2006 to Jan 2011 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 2002-2003. From Feb 2012 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 2008-2009.

N.D. = not detected.a = CPI(o/e)=(∑nCodd∑nCeven)b = Calculated as wax Cn = [Cn]-[(Cn+1)+(Cn-1)/2], [24]. Bold type = compound totals and maximum concentration for each compound group.Relative concentrations (%) of the different compounds from various propolis and asphalt samples collected from Riyadh (D) and Al-Bahah (C), Saudi Arabia.

Os dados de CPI: mudança de alimentos e bebidas não alcoólicas da Finlândia foram registrados em 0.852 % em 2025-01. Este é um registro de um aumento com relação aos números anteriores de 0.518 % em 2024-12. Os dados de CPI: mudança de alimentos e bebidas não alcoólicas da Finlândia são atualizados por mês, com uma média de 1.714 % em 2006-01 até 2025-01, com 229 observações. Os dados alcançaram um alto recorde de 16.269 % em 2023-02 e um baixo recorde de -6.247 % em 2010-06. Os dados de CPI: mudança de alimentos e bebidas não alcoólicas da Finlândia permanecem com status ativo na CEIC e são reportados pela fonte: CEIC Data. Os dados são classificados sob o World Trend Plus’ Global Economic Monitor – Table: CPI: Food and Non Alcoholic Beverage: Y-o-Y Growth: Monthly.

Brazil National Consumer Price Index (CPI): IPCA-E: Personal Expenses data was reported at 8,252.530 Dec1993=100 in Mar 2025. This records an increase from the previous number of 8,186.220 Dec1993=100 for Feb 2025. Brazil National Consumer Price Index (CPI): IPCA-E: Personal Expenses data is updated monthly, averaging 2,959.800 Dec1993=100 from Jan 1992 (Median) to Mar 2025, with 395 observations. The data reached an all-time high of 8,252.530 Dec1993=100 in Mar 2025 and a record low of 0.456 Dec1993=100 in Jan 1992. Brazil National Consumer Price Index (CPI): IPCA-E: Personal Expenses data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.IB052: Consumer Price Index: Special Broad Category - IPCA-E: POF 2008-2009: Dec 1993=100. IBGE published a historical series of price indices of IPCA-E kept with the same base year December 1993 = 100 with five different weighting structures over the entire period. From Nov 1991 to July 1999 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey - Pesquisa de Orçamentos Familiares (POF) 1987-1988. From August 1999 to June 2006 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 1995-1996. From July 2006 to Jan 2011 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 2002-2003. From Feb 2012 index, the weighting structures of indexes were obtained from the Consumer Expenditure Survey (POF) 2008-2009.