Cleaned Swiss smart meter data based on a collection from CKW AG (see opendata.ckw.ch)

Duration: 2 years (1. Jan 2021 - 31. Dec 2022, CET timestamp) Location: Canton Lucerne, Switzerland Interval: 15 minutes Values: Active Energy (kWh) Meters in each year: 4959 (see filtered_IDs.csv for all IDs) The original dataset has been filtered based

on missing data This means, all 4959 meters have consumption and reported values over the full duration of 2 years. Files are available as space-saving parquet files per day in year. Number in filename is number of day within the year.

Summary.zip contains summary statistics over all 112148 (unfiltered) meters counting observations (including duplicates), and aggregating energy data (per month, and or hourly data), see overview.csv within summary.zip.

Overview    This dataset offers valuable insights into yearly domestic water consumption across various Lower Super Output Areas (LSOAs) or Data Zones, accompanied by the count of water meters within each area. It is instrumental for analysing residential water use patterns, facilitating water conservation efforts, and guiding infrastructure development and policy making at a localised level. Key Definitions    Aggregation   The process of summarising or grouping data to obtain a single or reduced set of information, often for analysis or reporting purposes.     AMR Meter Automatic meter reading (AMR) is the technology of automatically collecting consumption, diagnostic, and status data from a water meter remotely and periodically. Dataset   Structured and organised collection of related elements, often stored digitally, used for analysis and interpretation in various fields.  Data Zone Data zones are the key geography for the dissemination of small area statistics in Scotland Dumb Meter A dumb meter or analogue meter is read manually. It does not have any external connectivity. Granularity   Data granularity is a measure of the level of detail in a data structure. In time-series data, for example, the granularity of measurement might be based on intervals of years, months, weeks, days, or hours   ID   Abbreviation for Identification that refers to any means of verifying the unique identifier assigned to each asset for the purposes of tracking, management, and maintenance.    LSOA Lower Layer Super Output Areas (LSOA) are a geographic hierarchy designed to improve the reporting of small area statistics in England and Wales. Open Data Triage   The process carried out by a Data Custodian to determine if there is any evidence of sensitivities associated with Data Assets, their associated Metadata and Software Scripts used to process Data Assets if they are used as Open Data.    Schema   Structure for organising and handling data within a dataset, defining the attributes, their data types, and the relationships between different entities. It acts as a framework that ensures data integrity and consistency by specifying permissible data types and constraints for each attribute.    Smart Meter A smart meter is an electronic device that records information and communicates it to the consumer and the supplier. It differs from automatic meter reading (AMR) in that it enables two-way communication between the meter and the supplier. Units   Standard measurements used to quantify and compare different physical quantities.  Water Meter Water metering is the practice of measuring water use. Water meters measure the volume of water used by residential and commercial building units that are supplied with water by a public water supply system. Data History    Data Origin    Domestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies. Data Triage Considerations   This section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements. Identification of Critical Infrastructure  This aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details. Commercial Risks and Anonymisation Individual Identification Risks There is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information. Meter and Property Association Challenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial. Interpretation of Null Consumption Instances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions. Meter Re-reads The dataset must account for instances where meters are read multiple times for accuracy. Joint Supplies & Multiple Meters per Household Special consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation. Schema Consistency with the Energy Industry: In formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above. After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset's schema with the standards established within the energy industry. This decision was influenced by the energy sector's experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection. Schema The dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters. Aggregation to Mitigate Risks The dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns. Data Freshness  Users should be aware that this dataset reflects historical consumption patterns and does not represent real-time data. Publish Frequency  Annually

The dataset presents an extensive overview of digital infrastructure development across various Indian cities from 2019 to 2024. This data provides critical insights into the country's journey toward becoming a digitally empowered society, emphasizing the implementation and expansion of smart city initiatives. The dataset covers key metrics such as household internet access, subscriptions to fixed and wireless broadband services, and the integration of smart technologies in essential utilities like water and electricity.

One of the focal points of the dataset is the increase in household internet access, highlighting the growing reach of digital connectivity. This metric reflects the steady progress made in extending internet services to urban populations, ensuring that more households can benefit from the internet's transformative power in education, employment, and communication. Moreover, the data tracks the growth in both fixed and wireless broadband subscriptions, allowing for an analysis of the penetration of different forms of internet access and the evolving preferences of consumers for either wired or mobile services.

Another important aspect of the dataset is the advancement of smart utility management, specifically in water and electricity. The data on smart meter installations offers insights into how Indian cities have adopted technology to better manage resources. These smart meters allow for real-time tracking of water and electricity consumption, improving efficiency, reducing wastage, and enabling both consumers and authorities to monitor and manage usage more effectively.

The dataset also captures the progress in smart public transportation systems, an essential component of any smart city initiative. Information systems providing real-time updates on public transport have become crucial for ensuring efficient urban mobility. These systems not only help commuters plan their journeys more effectively but also contribute to reducing traffic congestion and promoting the use of public transportation over private vehicles. Additionally, traffic monitoring technologies are covered in the dataset, showcasing the efforts to integrate surveillance and data analytics to manage urban traffic flows, reduce congestion, and improve overall road safety.

Furthermore, the dataset encompasses various other digital infrastructure metrics, painting a comprehensive picture of how Indian cities are evolving into smart, connected urban centers. By tracking this data over the five-year period from 2019 to 2024, stakeholders can assess the effectiveness of policy interventions, investments, and technological implementations aimed at enhancing the quality of life for urban residents.

Data HistoryData OriginDomestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies.Data Triage ConsiderationsThis section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements.Identification of Critical InfrastructureThis aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details.Individual Identification RisksThere is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information.Meter and Property AssociationChallenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial.Interpretation of Null ConsumptionInstances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions.Meter Re-readsThe dataset must account for instances where meters are read multiple times for accuracy.Joint Supplies & Multiple Meters per HouseholdSpecial consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation.Schema Consistency with the Energy Industry:In formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above.After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset"s schema with the standards established within the energy industry. This decision was influenced by the energy sector"s experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection.SchemaThe dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters.Aggregation to Mitigate RisksThe dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns.Data FreshnessUsers should be aware that this dataset reflects historical consumption patterns and does not represent real-time data.Publish FrequencyAnnuallyData Triage Review FrequencyAn annual review is conducted to ensure the dataset"s relevance and accuracy, with adjustments made based on specific requests or evolving data trends.Data SpecificationsFor the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include:Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption.Consumption for calendar year dates is calculated based on actual meter readings.​Meters of all types (smart, dumb, AMR) are included in this dataset.​The dataset is updated and published annually.​The dataset includes LSOAs with 10 or more meters. Any LSOAs with less than 10 meters have been excluded.​The dataset includes only meters that are currently shown as active.​The dataset excludes any meters where consumption is recorded as null.ContextUsers are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns.​The geographical data provided does not pinpoint locations of water meters within an LSOA.​​The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.​This dataset has been calculated using actual read data. To align reads to calendar year, our approach uses a combination of previous and next usage readings, along with the number of days between these readings to calculate the total consumption.Supplementary Information​Below is a curated selection of links for additional reading, which provide a deeper understanding of this dataset:Ofwat guidance on water meters Data SchemaDATA_SOURCE: Company that provided the dataYEAR: The calendar year covered by the dataLSOA_CODE: LSOA or Data Zone converted code of the meter locationNUMBER_OF_METERS: Number of meters within an LSOATOTAL_CONSUMPTION: Total consumption within the LSOATOTAL_CONSUMPTION_UNITS: Units for total consumption

Water meter dataset. Contains 1244 water meter images. Assembled using a crowdsourcing platform Yandex.Toloka.

The dataset consists of 1244 images. File name consists of: - water meter id - water meter readings

Acknowlegement

Foto von Sugarman Joe auf Unsplash

This dataset offers valuable insights into yearly domestic water consumption across various Lower Super Output Areas (LSOAs) or Data Zones, accompanied by the count of water meters within each area. It is instrumental for analysing residential water use patterns, facilitating water conservation efforts, and guiding infrastructure development and policy making at a localised level.Data HistoryData OriginDomestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies.Data Triage ConsiderationsThis section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements.Identification of Critical InfrastructureThis aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details.Commercial Risks and AnonymisationIndividual Identification RisksThere is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information.Meter and Property AssociationChallenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial.Interpretation of Null ConsumptionInstances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions.Meter Re-readsThe dataset must account for instances where meters are read multiple times for accuracy.Joint Supplies & Multiple Meters per HouseholdSpecial consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation.Schema Consistency with the Energy IndustryIn formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above.After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset's schema with the standards established within the energy industry. This decision was influenced by the energy sector's experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection.SchemaThe dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters.Aggregation to Mitigate RisksThe dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns.Data FreshnessUsers should be aware that this dataset reflects historical consumption patterns and does not represent real-time data.Publish FrequencyAnnually (1st of April to the 31st of March)Data Triage Review FrequencyAn annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends.Data SpecificationsFor the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include:Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption.Where it is necessary to estimate consumption, this is calculated based on actual meter readings.Meters of all types (smart, dumb, AMR) are included in this dataset.The dataset is updated and published annually (1st of April to the 31st of March).Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release.The dataset includes LSOAs with 10 or more meters. Any LSOAs with less than 10 meters have been excluded.ContextUsers are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns.The geographical data provided does not pinpoint locations of water meters within an LSOA.The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.Supplementary InformationBelow is a curated selection of links for additional reading, which provide a deeper understanding of this dataset:Stream - The Value of a Domestic Consumption Data Use Case: https://www.streamwaterdata.co.uk/apps/0742154c5c1547cb8df235ccf2f66d57/exploreOfwat - Guidance on water meters: https://www.ofwat.gov.uk/wp-content/uploads/2015/11/prs_lft_101117meters.pdf

This is grouped and aggregated electricity consumption data from the EtudELEC study conducted by the Observatoire du Transition Energétique Grenoble (OTE-UGA). If you find this dataset useful and like different groupings to be published or have any questions, please feel free to comment on the discussion dedicated to this dataset on the OTE forum . The EtudELEC study only studies electricity consumption from residential dwellings around France. The study involved over 400 homes (individual houses and apartments) and spanned ~11 months from 25th October 2022 to 1st October 2023. The data is collected from the smart meter data (Linky data) which is only available with a time-step of 30 minutes. The data is in average watts consumed in a half-hour period. For reasons for privacy in line with GDPR laws, personal data such as individual home consumption will be shared as aggregated datasets as opposed to individual data points. The data from the participants were aggregated based on the following groupings: - Type of heating used and type of residence (stand-alone house vs apartment) This dataset is best viewed in the "Tree" view below. A folder is created for each of the groupings and sub-folders exist for all the subsequent groups. Each group folder contains: - a table of the minimum, mean, and maximum of the average power consumed for each 30-minute period (W), and - a JSON file with aggregated demographics information (number of inhabitants in different age backets, socio-professional category, year of construction etc.) of the group The datasets will be updated on a yearly basis following the renewal of consent of the panel members. Il s'agit de données de consommation électriques groupées et agrégées issues de l'étude EtudELEC menée par l'Observatoire de la Transition Energétique (OTE-UGA). Si vous trouvez ce jeu de données utile et souhaitez que différents regroupements soient publiés, n'hésitez pas à écrire dans le topic sur le forum OTE. L'étude EtudELEC est une étude sur la consommation d'électricité des logements résidentiels en France. L'étude porte sur plus de 400 logements (maisons individuelles et appartements) et s'étend sur 11 mois du 25 octobre 2022 au 1 octobre 2023. Les données sont collectées à partir des données des compteurs intelligents (données Linky) qui ne sont disponibles qu'avec un pas de temps de 30 minutes. Les données sont exprimées en watts consommés en moyenne sur une période d'une demi-heure. Pour des raisons de confidentialité conformes aux lois RGPD, les données personnelles telles que la consommation individuelle des maisons seront partagées sous forme d'ensembles de données agrégées plutôt que de points de données individuels. Les données des participants ont été agrégées sur la base des regroupements suivants : - Type de chauffage utilisé et type de résidence (maison individuelle ou appartement). Cet ensemble de données est mieux visualisé dans l'arborescence ci-dessous. Un dossier est créé pour chaque groupe et des sous-dossiers existent pour tous les groupes suivants. Chaque dossier de groupe contient : - un tableau du minimum, de la moyenne et du maximum de la puissance moyenne consommée pour chaque période de 30 minutes (W), et - un fichier JSON avec des informations démographiques agrégées (nombre d'habitants dans différentes tranches d'âge, catégorie socioprofessionnelle, année de construction, etc. Les jeux de données seront mis à jour chaque année après le renouvellement du consentement des membres du panel.

Data History

Data Origin

Domestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies.

Data Triage Considerations

This section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements.

Identification of Critical Infrastructure

This aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details.

Commercial Risks and Anonymisation Individual Identification Risks

There is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information.

Meter and Property Association

Challenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial.

Interpretation of Null Consumption

Instances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions.

Meter Re-reads

The dataset must account for instances where meters are read multiple times for accuracy.

Joint Supplies & Multiple Meters per Household

Special consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation.

Schema Consistency with the Energy Industry

In formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above.

After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset's schema with the standards established within the energy industry. This decision was influenced by the energy sector's experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection.

Schema The dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters.

Aggregation to Mitigate Risks The dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns.

Data Freshness Users should be aware that this dataset reflects historical consumption patterns and does not represent real-time data. Publish Frequency Weekly.

Data Triage Review Frequency An annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends.

Data Specifications For the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include: • Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption. • Where it is necessary to estimate consumption, this is calculated based on actual meter readings. • Meters of all types (smart, dumb, AMR) are included in this dataset. • The dataset is updated and published Weekly. • Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release. • The dataset includes LSOAs with 2 or more meters. Any LSOAs with less than 2 meters have been excluded. • Consumption data is only included where we have the full consumption data for a year for a given meter.

Context Users are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns.

The geographical data provided does not pinpoint locations of water meters within an LSOA.

The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.

Supplementary InformationBelow is a curated selection of links for additional reading, which provide a deeper understanding of this dataset.1.Ofwat guidance on water meters. https://www.ofwat.gov.uk/wp-content/uploads/2015/11/prs_lft_101117meters.pdf Data Schema DATA_SOURCE: Company that provided the data YEAR: The calendar year covered by the data LSOA_CODE: LSOA or Data Zone converted code of the meter location NUMBER_OF_METERS: Number of meters within an LSOA TOTAL_CONSUMPTION: Average consumption within the LSOA TOTAL_CONSUMPTION_UNITS: Units for average consumption

This table shows regional figures on the average consumption of energy (natural gas and electricity) of private dwellings broken down by type of dwelling and ownership for Nederland, group of provinces, provinces and municipalities. Besides, for total dwellings only, the share of heat distribution (district heating) has been added, because this is relevant for the interpretation of the height of the average consumption of natural gas.

Data available from: 2010

Status of the figures: All figures from 2010 - 2021 are definite. Figures of 2022 and 2023 are revised provisional. Figures for 2024 are provisional.

Changes as of September 2025: Figures added for 2024. Figures for 2022 and 2023 have been revised based on smart-meter data. These figures are more accurate than figures based on standard yearly consumption data.

Changes as of October 2023: Provisional figures of 2022 have been added. Figures of 2021 have been updated. The category “Average consumption of electricity” is replaced by “Average supply of electricity” and a category “Average net supply of electricity” has been added.

When will new figures be published? A revision to the method of this statistic is currently underway, causing the table to be delayed. New figures will come in the 3rd quarter of the folowing year.

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.

Smart Metering System (SMS) Fueling Industry's Future?

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