Consumer Confidence in the United States decreased to 58.20 points in August from 61.70 points in July of 2025. This dataset provides the latest reported value for - United States Consumer Sentiment - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In December 2024, the Consumer Confidence Index (CCI) of the United States stood at *****. The CCI in the U.S. began to slowly increase over the later half of 2024 after a year of decline. The CCI is an indicator of the confidence of consumers regarding their expected financial situation and their likelihood to spend money in the next 12 months. A CCI value above 100 indicates an increase in consumer confidence and the chance that consumers will spend money on major purchases in the next year. A value below 100 indicates negative economic developments, as consumers are likely to save their money.

Turkey Consumer Confidence Index: Assessment: Consumer Price Change data was reported at 41.266 Point in Nov 2018. This records an increase from the previous number of 40.686 Point for Oct 2018. Turkey Consumer Confidence Index: Assessment: Consumer Price Change data is updated monthly, averaging 70.780 Point from Jan 2012 (Median) to Nov 2018, with 83 observations. The data reached an all-time high of 89.112 Point in Aug 2012 and a record low of 40.686 Point in Oct 2018. Turkey Consumer Confidence Index: Assessment: Consumer Price Change data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.H028: Consumer Confidence Index.

Turkey Consumer Confidence Index: Expect: Consumer Price Chg: Next 12 Mth data was reported at 79.261 Point in Nov 2018. This records an increase from the previous number of 74.727 Point for Oct 2018. Turkey Consumer Confidence Index: Expect: Consumer Price Chg: Next 12 Mth data is updated monthly, averaging 83.396 Point from Jan 2012 (Median) to Nov 2018, with 83 observations. The data reached an all-time high of 96.008 Point in Aug 2012 and a record low of 71.746 Point in Aug 2018. Turkey Consumer Confidence Index: Expect: Consumer Price Chg: Next 12 Mth data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.H028: Consumer Confidence Index.

Consumer Price Index CPI in Egypt decreased to 254.20 points in June from 254.50 points in May of 2025. This dataset provides - Egypt Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Turkey CI: sa: Assessment: Consumer Price Change data was reported at 48.963 Point in Apr 2020. This records an increase from the previous number of 42.061 Point for Mar 2020. Turkey CI: sa: Assessment: Consumer Price Change data is updated monthly, averaging 69.501 Point from Jan 2012 (Median) to Apr 2020, with 100 observations. The data reached an all-time high of 81.011 Point in Feb 2012 and a record low of 31.353 Point in Jun 2019. Turkey CI: sa: Assessment: Consumer Price Change data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.H029: Consumer Confidence Index: Seasonally Adjusted. [COVID-19-IMPACT]

Turkey CI: sa: Expectation: Consumer Price Change: Next 12 Mth data was reported at 72.498 Point in Apr 2020. This records a decrease from the previous number of 73.755 Point for Mar 2020. Turkey CI: sa: Expectation: Consumer Price Change: Next 12 Mth data is updated monthly, averaging 81.843 Point from Jan 2012 (Median) to Apr 2020, with 100 observations. The data reached an all-time high of 96.008 Point in Aug 2012 and a record low of 71.537 Point in Jan 2020. Turkey CI: sa: Expectation: Consumer Price Change: Next 12 Mth data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.H029: Consumer Confidence Index: Seasonally Adjusted. [COVID-19-IMPACT]

The Consumer Price Index (CPI) is a key economic indicator used by policymakers worldwide to monitor inflation and guide monetary policy decisions. In Korea, the CPI significantly impacts decisions on interest rates, fiscal policy frameworks, and the Bank of Korea’s strategies for economic stability. Given its importance, accurately forecasting the Total CPI is crucial for informed decision-making. Achieving accurate estimation, however, presents several challenges. First, the Korean Total CPI is calculated as a weighted sum of 462 items grouped into 12 categories of goods and services. This heterogeneity makes it difficult to account for all variations in consumer behavior and price dynamics. Second, the monthly frequency of CPI data results in a relatively sparse time series, limiting the performance of the analysis. Furthermore, external factors such as policy changes and pandemics add further volatility to the CPI. To address these challenges, we propose a novel framework consisting of four key components: (1) a hybrid Convolutional Neural Network-Long Short-Term Memory mechanism designed to capture complex patterns in CPI data, enhancing estimation accuracy; (2) multivariate inputs that incorporate CPI component indices alongside auxiliary variables for richer contextual information; (3) data augmentation through linear interpolation to convert monthly data into daily data, optimizing it for highly parametrized deep learning models; and (4) sentiment index derived from Korean CPI-related news articles, providing insights into external factors influencing CPI fluctuations. Experimental results demonstrate that the proposed model outperforms existing approaches in CPI prediction, as evidenced by lower RMSE values. This improved accuracy has the potential to support the development of more timely and effective economic policies.

Consumer Price Index CPI in Moldova increased to 4545.26 points in July from 4544.41 points in June of 2025. This dataset provides - Moldova Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

In this paper, we introduce the mixed-frequency data model (MIDAS) to China’s insurance demand forecasting. We select the monthly indicators Consumer Confidence Index (CCI), China Economic Policy Uncertainty Index (EPU), Consumer Price Index (PPI), and quarterly indicator Depth of Insurance (TID) to construct a Mixed Data Sampling (MIDAS) regression model, which is used to study the impact and forecasting effect of CCI, EPU, and PPI on China’s insurance demand. To ensure forecasting accuracy, we investigate the forecasting effects of the MIDAS models with different weighting functions, forecasting windows, and a combination of forecasting methods, and use the selected optimal MIDAS models to forecast the short-term insurance demand in China. The experimental results show that the MIDAS model has good forecasting performance, especially in short-term forecasting. Rolling window and recursive identification prediction can improve the prediction accuracy, and the combination prediction makes the results more robust. Consumer confidence is the main factor influencing the demand for insurance during the COVID-19 period, and the demand for insurance is most sensitive to changes in consumer confidence. Shortly, China’s insurance demand is expected to return to the pre-COVID-19 level by 2023Q2, showing positive development. The findings of the study provide new ideas for China’s insurance policymaking.

Data Set Information:

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

There are four datasets: 1) bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014] 2) bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. 3) bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). 4) bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs). The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM).

The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).

Attribute Information:

Input variables:

bank client data:

1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown')

related with the last contact of the current campaign:

8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

other attributes:

12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

social and economic context attributes

16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric)

Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')

Relevant Papers:

S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimaraes, Portugal, October, 2011. EUROSIS. [bank.zip]

Consumer Price Index CPI in Puerto Rico increased to 137.89 points in June from 137.80 points in May of 2025. This dataset provides - Puerto Rico Consumer Price Index Cpi- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Bank Marketing

Abstract: The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).

Data Set Information: The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

Attribute Information:

Bank client data:

• Age (numeric)
• Job : type of job (categorical: 'admin.', 'blue-collar', 'entrepreneur', 'housemaid', 'management', 'retired', 'self-employed', 'services', 'student', 'technician', 'unemployed', 'unknown')
• Marital : marital status (categorical: 'divorced', 'married', 'single', 'unknown' ; note: 'divorced' means divorced or widowed)
• Education (categorical: 'basic.4y', 'basic.6y', 'basic.9y', 'high.school', 'illiterate', 'professional.course', 'university.degree', 'unknown')
• Default: has credit in default? (categorical: 'no', 'yes', 'unknown')
• Housing: has housing loan? (categorical: 'no', 'yes', 'unknown')
• Loan: has personal loan? (categorical: 'no', 'yes', 'unknown')

Related with the last contact of the current campaign:

• Contact: contact communication type (categorical: 'cellular','telephone')
• Month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')
• Day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')
• Duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

Other attributes:

• Campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
• Pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)
• Previous: number of contacts performed before this campaign and for this client (numeric)
• Poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

Social and economic context attributes

• Emp.var.rate: employment variation rate - quarterly indicator (numeric)
• Cons.price.idx: consumer price index - monthly indicator (numeric)
• Cons.conf.idx: consumer confidence index - monthly indicator (numeric)
• Euribor3m: euribor 3 month rate - daily indicator (numeric)
• Nr.employed: number of employees - quarterly indicator (numeric)

Output variable (desired target):

• y - has the client subscribed a term deposit? (binary: 'yes', 'no')

Analysis Steps:

• Atribute information Analysis.
• Machine Learning (Logistic Regression, KNN, SVM, Decision Tree,
  Random Forest, Naive Bayes)
• Deep Learning (ANN)

Source:

• Dataset from : http://archive.ics.uci.edu/ml/datasets/Bank+Marketing#

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Attribute Information:

Input variables: -> bank client data: 1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown') -> related with the last contact of the current campaign: 8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

->0ther attributes: 12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

->Social and economic context attributes 16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric)

->Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')

Relevant Tasks;

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Relevant Papers;

Consumer Price Index CPI in Bolivia increased to 142.88 points in July from 141.19 points in June of 2025. This dataset provides - Bolivia Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Consumer Price Index CPI in Uruguay increased to 113.37 points in July from 113.31 points in June of 2025. This dataset provides - Uruguay Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Consumer Price Index CPI in Kuwait increased to 137.20 points in July from 136.90 points in June of 2025. This dataset provides - Kuwait Consumer Price Index Cpi- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Consumer Price Index CPI in El Salvador increased to 131.29 points in July from 130.86 points in June of 2025. This dataset provides - El Salvador Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Consumer Price Index CPI in Tunisia increased to 186.70 points in July from 186.10 points in June of 2025. This dataset provides - Tunisia Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.