Inflation Rate in the United States increased to 3 percent in September from 2.90 percent in August of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains 30,000 sequential trading-day records, generated synthetically to replicate realistic long-term stock market behavior. It includes essential financial time-series variables such as open and close prices, intraday highs and lows, volume, daily percentage returns, and volatility range.

To support advanced financial analytics and machine-learning models, the dataset also includes widely used technical indicators such as:

SMA-50 (Simple Moving Average) RSI-14 (Relative Strength Index) MACD (Moving Average Convergence Divergence) Bollinger Upper Band (20-day SMA + 2×STD)

The dataset is ideal for: Price forecasting (regression models) Up/Down day classification (ML classification) Volatility modeling Algorithmic trading simulations Time-series forecasting (LSTM, ARIMA, Prophet, Transformers) Quantitative finance education Financial dashboards and visualization Feature engineering demonstrations

All data is fully synthetic, statistically realistic, and safe for public use.

COLUMN DESCRIPTIONS

Date The business day of the trading session. Primary time index.

Open_Price Price at market open.

Close_Price Price at market close; often the key forecasting target.

High_Price Highest price reached during the trading day.

Low_Price Lowest price reached during the trading day.

Volume Total number of shares traded. Reflects market activity.

Daily_Return_Pct Percentage return relative to previous close. Suitable for up/down day prediction.

Volatility_Range Intraday price difference (High − Low). Measures daily volatility.

SMA_50 50-day Simple Moving Average. Indicates medium-term trend direction.

RSI_14 14-day Relative Strength Index. Shows momentum and overbought/oversold conditions.

MACD_Value Difference between 12-day EMA and 26-day EMA. Used to signal trend shifts.

Bollinger_Bands_Upper Upper Bollinger Band (20-day SMA + 2×STD). Highlights volatility and price extremes.

Gold fell to 4,199.97 USD/t.oz on December 2, 2025, down 0.75% from the previous day. Over the past month, Gold's price has risen 4.93%, and is up 58.92% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on December of 2025.

Core consumer prices in the United States increased 3 percent in September of 2025 over the same month in the previous year. This dataset provides - United States Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Crude Oil fell to 59.17 USD/Bbl on December 2, 2025, down 0.25% from the previous day. Over the past month, Crude Oil's price has fallen 3.08%, and is down 15.40% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on December of 2025.

This data set covers Ministerial foreign travel, home travel and subsistence. The mission of the Department’s Foreign Travel Section is to provide for the foreign travel needs, both travel and accommodation, of Ministers of the Department of Housing, Local Government and Heritage. Travel and subsistence expenses not covered by the Foreign Travel Section can be claimed through NSSO separately by Ministers on official business – for either home travel or foreign travel. These costs are then charged back to the Department. In some instances costs are covered by Department of Foreign Affairs which are then charged back to the Department. Ministers will be reimbursed expenditure necessarily incurred in the course of official duty away from home or headquarters. Where possible, all travel should be by the shortest practicable routes and by the cheapest practicable mode of conveyance. This data set is updated quarterly but all costs may not be included until advised to the Department. Blank columns indicate costs still to be confirmed/determined.

Quickstart

https://colab.research.google.com/drive/1W6TprjcxOdXsNwswkpm_XX2U_xld9_zZ#offline=true&sandboxMode=true

Context

Predicting the stock market is a game as old as the stock market itself. On popular ML platforms like Kaggle, users often compete to come up with highly nuanced, optimized models to solve the stock market starting just from price data. LSTMs may end up being the most effective model, but the real problem isn't the model - it's the data.

Human and algorithmic traders in the financial industry know this, and augment their datasets with lots of useful information about stocks called "technical indicators". These indicators have fancy sounding names - e.g. the "Aroon Oscillator" and the "Chaikin Money Flow Index", but most boil down to simple calculations involving moving averages and volatility. Access to these indicators is unrestricted for humans (you can view them on most trading platforms), but access to well formatted indicators (csvs instead of visual lines) for large datasets reaching back significantly in time is nearly impossible to find. Even if you pay for a service, API usage limits make putting together such a dataset prohibitively expensive.

The fact that this information is largely kept behind paywalls for large firms with proprietary resources makes me question the fairness of this market. With a data imbalance like this, how can a single trader - a daytrader - expect to make money? I wanted to make this data available to the ML community because it is my hope that bringing this data to the community will help to even the scales. Whether you're just looking to toy around and make a few bucks, or interested in contributing to something larger - a group of people working to develop algorithms to help the "little guy" trade - I hope this dataset will be helpful. To the best of my knowledge, this is the first dataset of its kind, but I hope it is not the last.

Data

• I'd recommend starting here: https://colab.research.google.com/drive/1W6TprjcxOdXsNwswkpm_XX2U_xld9_zZ#offline=true&sandboxMode=true In this notebook, I've uploaded the indicators data and made it available for read access. The notebook will walk you through processing the data and putting it to work building advanced ML models.

• If you'd like to download it directly, you can do so here (31GB): https://drive.google.com/file/d/1HVKWtLWlIZj5yY4T3R1CDSn3c_Nh1lB5/view?usp=sharing

• For legal reasons, I use borismarjanovic's dataset as a baseline, which ends in 2017. If you'd like to update your prices and indicators so your model can trade day to day, I've included instructions and code in the quickstart that should be helpful.

Acknowledgements

• The many online tutorials and specifications which helped me write and test the indicator functions
• borismarjanovic for making public an amazing dataset that I use as a baseline for the colab notebook and the direct download file above
• The many online services that have allowed me to download all the recent price information to augment Boris' dataset (which legally I cannot share, but which helped me develop the infrastructure to update the indicators given new prices data that I share in the quickstart and repo).

Next Steps / Future Directions

• Building inventive models using this dataset to more and more accurately predict stock price movements
• Incorporating arbitrage analysis across stocks
• Hedging
• Options and selling short
• Commodities, currencies, ETFs

Collaboration

If this interests you, reach out! My email is abwilf [at] umich [dot] edu. The repository I used to generate the dataset is here: https://github.com/abwilf/daytrader. I love forks. If you want to work on the project, send me a pull request!

Fault Lines Widen in the Global Recovery

Economic prospects have diverged further across countries since the April 2021 World Economic Outlook (WEO) forecast. Vaccine access has emerged as the principal fault line along which the global recovery splits into two blocs: those that can look forward to further normalization of activity later this year (almost all advanced economies) and those that will still face resurgent infections and rising COVID death tolls. The recovery, however, is not assured even in countries where infections are currently very low so long as the virus circulates elsewhere.

The global economy is projected to grow 6.0 percent in 2021 and 4.9 percent in 2022.The 2021 global forecast is unchanged from the April 2021 WEO, but with offsetting revisions. Prospects for emerging market and developing economies have been marked down for 2021, especially for Emerging Asia. By contrast, the forecast for advanced economies is revised up. These revisions reflect pandemic developments and changes in policy support. The 0.5 percentage-point upgrade for 2022 derives largely from the forecast upgrade for advanced economies, particularly the United States, reflecting the anticipated legislation of additional fiscal support in the second half of 2021 and improved health metrics more broadly across the group.

Recent price pressures for the most part reflect unusual pandemic-related developments and transitory supply-demand mismatches. Inflation is expected to return to its pre-pandemic ranges in most countries in 2022 once these disturbances work their way through prices, though uncertainty remains high. Elevated inflation is also expected in some emerging market and developing economies, related in part to high food prices. Central banks should generally look through transitory inflation pressures and avoid tightening until there is more clarity on underlying price dynamics. Clear communication from central banks on the outlook for monetary policy will be key to shaping inflation expectations and safeguarding against premature tightening of financial conditions. There is, however, a risk that transitory pressures could become more persistent and central banks may need to take preemptive action.

Risks around the global baseline are to the downside. Slower-than-anticipated vaccine rollout would allow the virus to mutate further. Financial conditions could tighten rapidly, for instance from a reassessment of the monetary policy outlook in advanced economies if inflation expectations increase more rapidly than anticipated. A double hit to emerging market and developing economies from worsening pandemic dynamics and tighter external financial conditions would severely set back their recovery and drag global growth below this outlook’s baseline.

Multilateral action has a vital role to play in diminishing divergences and strengthening global prospects. The immediate priority is to deploy vaccines equitably worldwide. A $50 billion IMF staff proposal, jointly endorsed by the World Health Organization, World Trade Organization, and World Bank, provides clear targets and pragmatic actions at a feasible cost to end the pandemic. Financially constrained economies also need unimpeded access to international liquidity. The proposed $650 billion General Allocation of Special Drawing Rights at the IMF is set to boost reserve assets of all economies and help ease liquidity constraints. Countries also need to redouble collective efforts to reduce greenhouse gas emissions. These multilateral actions can be reinforced by national-level policies tailored to the stage of the crisis that help catalyze a sustainable, inclusive recovery. Concerted, well-directed policies can make the difference between a future of durable recoveries for all economies or one with widening fault lines—as many struggle with the health crisis while a handful see conditions normalize, albeit with the constant threat of renewed flare-ups.

This data set covers Ministerial foreign travel, home travel and subsistence. The mission of the Department’s Foreign Travel Section is to provide for the foreign travel needs, both travel and accommodation, of Ministers of the Department of Housing, Local Government and Heritage. Travel and subsistence expenses not covered by the Foreign Travel Section can be claimed through NSSO separately by Ministers on official business – for either home travel or foreign travel. These costs are then charged back to the Department. In some instances costs are covered by Department of Foreign Affairs which are then charged back to the Department. Ministers will be reimbursed expenditure necessarily incurred in the course of official duty away from home or headquarters. Where possible, all travel should be by the shortest practicable routes and by the cheapest practicable mode of conveyance. This data set is updated quarterly but all costs may not be included until advised to the Department. .hidden { display: none }

Dataset of historical annual silver prices from 1970 to 2022, including significant events and acts that impacted silver prices.

E-Commerce Sales Dataset

Analyzing and Maximizing Online Business Performance

By ANil [source]

About this dataset

This dataset provides an in-depth look at the profitability of e-commerce sales. It contains data on a variety of sales channels, including Shiprocket and INCREFF, as well as financial information on related expenses and profits. The columns contain data such as SKU codes, design numbers, stock levels, product categories, sizes and colors. In addition to this we have included the MRPs across multiple stores like Ajio MRP , Amazon MRP , Amazon FBA MRP , Flipkart MRP , Limeroad MRP Myntra MRP and PaytmMRP along with other key parameters like amount paid by customer for the purchase , rate per piece for every individual transaction Also we have added transactional parameters like Date of sale months category fulfilledby B2b Status Qty Currency Gross amt . This is a must-have dataset for anyone trying to uncover the profitability of e-commerce sales in today's marketplace

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How to use the dataset

This dataset provides a comprehensive overview of e-commerce sales data from different channels covering a variety of products. Using this dataset, retailers and digital marketers can measure the performance of their campaigns more accurately and efficiently.

The following steps help users make the most out of this dataset: - Analyze the general sales trends by examining info such as month, category, currency, stock level, and customer for each sale. This will give you an idea about how your e-commerce business is performing in each channel.
- Review the Shiprocket and INCREF data to compare and analyze profitability via different fulfilment methods. This comparison would enable you to make better decisions towards maximizing profit while minimizing costs associated with each method’s referral fees and fulfillment rates.
- Compare prices between various channels such as Amazon FBA MRP, Myntra MRP, Ajio MRP etc using the corresponding columns for each store (Amazon MRP etc). You can judge which stores are offering more profitable margins without compromising on quality by analyzing these pricing points in combination with other information related to product sales (TP1/TP2 - cost per piece).
- Look at customer specific data such as TP 1/TP 2 combination wise Gross Amount or Rate info in terms price per piece or total gross amount generated by any SKU dispersed over multiple customers with relevant dates associated to track individual item performance relative to others within its category over time periods shortlisted/filtered appropriately.. Have an eye on items commonly utilized against offers or promotional discounts offered hence crafting strategies towards inventory optimization leading up-selling operations.?
- Finally Use Overall ‘Stock’ details along all the P & L Data including Yearly Expenses_IIGF information record for takeaways which might be aimed towards essential cost cutting measures like switching amongst delivery options carefully chosen out of Shiprocket & INCREFF leadings away from manual inspections catering savings under support personnel outsourcing structures.?

By employing a comprehensive understanding on how our internal subsidiaries perform globally unless attached respective audits may provide us remarkably lower operational costs servicing confidence; costing far lesser than being incurred taking into account entire pallet shipments tracking sheets representing current level supply chains efficiencies achieved internally., then one may finally scale profits exponentially increases cut down unseen losses followed up introducing newer marketing campaigns necessarily tailored according playing around multiple goods based spectrums due powerful backing suitable transportation boundaries set carefully

Research Ideas

• Analysing the difference in profitability between sales made through Shiprocket and INCREFF. This data can be used to see where the biggest profit margins lie, and strategize accordingly.
• Examining the Complete Cost structure of a product with all its components and their contribution towards revenue or profitability, i.e., TP 1 & 2, MRP Old & Final MRP Old together with Platform based MRP - Amazon, Myntra and Paytm etc., Currency based Profit Margin etc.
• Building a predictive model using Machine Learning by leveraging historical data to predict future sales volume and profits for e-commerce products across multiple categories/devices/platforms such as Amazon, Flipkart, Myntra etc as well providing m...

This map presents the full data available on the MLTSD GeoHub, and maps several of the key variables reflected by the Second Career Program of ETD.The Second Career program provides training to unemployed or laid-off individuals to help them find employment in high demand occupations in Ontario. The intention of the SC program is to return individuals to employment by the most cost effective path. Second Career provides up to $28,000 to assist laid-off workers with training-related costs such as tuition, books, transportation, and basic living expenses, based on individual need. Additional allowances may be available for people with disabilities, and for clients needing help with the costs of dependent care, living away from home and literacy and basic skills upgrading, also based on individual need. People with disabilities may also be given extensions on training and upgrading durations, to meet their specific needs. Clients may be required to contribute to their skills training, based on the client’s total annual gross household income and the number of household members.About This DatasetThis dataset contains data on SC clients for each of the twenty-six Local Board (LB) areas in Ontario for the 2015/16 fiscal year, based on data provided to Local Boards and Local Employment Planning Councils (LEPC) in June 2016 (see below for details on Local Boards). These clients have been distributed across Local Board areas based on the client’s home address, not the address of their training institution(s).Different variables in this dataset cover different groups of Second Career clients, as follows:Demographic and skills training variables are composed of all SC clients that started in 2015/16.At exit outcome variables are composed of all SC clients that completed their program in 2015/16.12-month outcome variables are composed of all SC clients that completed a 12-month survey in 2015/16.The specific variables that fall into each of the above categories are detailed in the Technical Dictionary. As a result of these differences, not all variables in this dataset are comparable to the other variables in this dataset; for example, the outcomes at exit data is not the outcomes for the clients described by the demographic variables.About Local BoardsLocal Boards are independent not-for-profit corporations sponsored by the Ministry of Labour, Training and Skills Development to improve the condition of the labour market in their specified region. These organizations are led by business and labour representatives, and include representation from constituencies including educators, trainers, women, Francophones, persons with disabilities, visible minorities, youth, Indigenous community members, and others. For the 2015/16 fiscal year there were twenty-six Local Boards, which collectively covered all of the province of Ontario. The primary role of Local Boards is to help improve the conditions of their local labour market by:engaging communities in a locally-driven process to identify and respond to the key trends, opportunities and priorities that prevail in their local labour markets;facilitating a local planning process where community organizations and institutions agree to initiate and/or implement joint actions to address local labour market issues of common interest;creating opportunities for partnership development activities and projects that respond to more complex and/or pressing local labour market challenges; andorganizing events and undertaking activities that promote the importance of education, training and skills upgrading to youth, parents, employers, employed and unemployed workers, and the public in general.In December 2015, the government of Ontario launched an eighteen-month Local Employment Planning Council pilot program, which established LEPCs in eight regions in the province formerly covered by Local Boards. LEPCs expand on the activities of existing Local Boards, leveraging additional resources and a stronger, more integrated approach to local planning and workforce development to fund community-based projects that support innovative approaches to local labour market issues, provide more accurate and detailed labour market information, and develop detailed knowledge of local service delivery beyond Employment Ontario (EO).Eight existing Local Boards were awarded LEPC contracts that were effective as of January 1st, 2016. As such, from January 1st, 2016 to March 31st, 2016, these eight Local Boards were simultaneously Local Employment Planning Councils. The eight Local Boards awarded contracts were:Durham Workforce AuthorityPeel-Halton Workforce Development GroupWorkforce Development Board - Peterborough, Kawartha Lakes, Northumberland, HaliburtonOttawa Integrated Local Labour Market PlanningFar Northeast Training BoardNorth Superior Workforce Planning BoardElgin Middlesex Oxford Workforce Planning & Development BoardWorkforce Windsor-EssexMLTSD has provided Local Boards and LEPCs with demographic and outcome data for clients of Employment Ontario (EO) programs delivered by service providers across the province on an annual basis since June 2013. This was done to assist Local Boards in understanding local labour market conditions. These datasets may be used to facilitate and inform evidence-based discussions about local service issues – gaps, overlaps and under-served populations - with EO service providers and other organizations as appropriate to the local context.Data on the following EO programs for the 2015/16 fiscal year was made available to Local Boards and LEPCs in June 2016: Employment Services (ES)Literacy and Basic Skills (LBS) Second Career (SC) ApprenticeshipThis dataset contains the 2015/16 SC data that was sent to Local Boards and LEPCs. Datasets covering past fiscal years will be released in the future.Terms and Definitions

NOC – The National Organizational Classification (NOC) is an occupational classification system developed by Statistics Canada and Human Resources and Skills Development Canada to provide a standard lexicon to describe and group occupations in Canada primarily on the basis of the work being performed in the occupation. It is a comprehensive system that encompasses all occupations in Canada in a hierarchical structure. At the highest level are ten broad occupational categories, each of which has a unique one-digit identifier. These broad occupational categories are further divided into forty major groups (two-digit codes), 140 minor groups (three-digit codes), and 500 unit groups (four-digit codes). This dataset uses four-digit NOC codes from the 2011 edition to identify the training programs of Second Career clients.Notes

Data reporting on 5 individuals or less has been suppressed to protect the privacy of those individuals.Data published: Feb 1, 2017Publisher: Ministry of Labour, Training and Skills Development (MLTSD)Update frequency: Yearly Geographical coverage: Ontario

The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

1. Title: 1985 Auto Imports Database

2. Source Information: -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) -- Date: 19 May 1987 -- Sources: 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook. 2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037

3. Past Usage: -- Kibler,~D., Aha,~D.~W., & Albert,~M. (1989). Instance-based prediction of real-valued attributes. {\it Computational Intelligence}, {\it 5}, 51--57. -- Predicted price of car using all numeric and Boolean attributes -- Method: an instance-based learning (IBL) algorithm derived from a localized k-nearest neighbor algorithm. Compared with a linear regression prediction...so all instances with missing attribute values were discarded. This resulted with a training set of 159 instances, which was also used as a test set (minus the actual instance during testing). -- Results: Percent Average Deviation Error of Prediction from Actual -- 11.84% for the IBL algorithm -- 14.12% for the resulting linear regression equation

4. Relevant Information: -- Description This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process "symboling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.

   The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc...), and represents the average loss per car per year.

   -- Note: Several of the attributes in the database could be used as a "class" attribute.

5. Number of Instances: 205

6. Number of Attributes: 26 total -- 15 continuous -- 1 integer -- 10 nominal

7. Attribute Information:
   Attribute: Attribute Range:

   1. symboling: -3, -2, -1, 0, 1, 2, 3.
   2. normalized-losses: continuous from 65 to 256.
   3. make: alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz, mercury, mitsubishi, nissan, peugot, plymouth, porsche, renault, saab, subaru, toyota, volkswagen, volvo
   4. fuel-type: diesel, gas.
   5. aspiration: std, turbo.
   6. num-of-doors: four, two.
   7. body-style: hardtop, wagon, sedan, hatchback, convertible.
   8. drive-wheels: 4wd, fwd, rwd.
   9. engine-location: front, rear.
   10. wheel-base: continuous from 86.6 120.9.
   11. length: continuous from 141.1 to 208.1.
   12. width: continuous from 60.3 to 72.3.
   13. height: continuous from 47.8 to 59.8.
   14. curb-weight: continuous from 1488 to 4066.
   15. engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor.
   16. num-of-cylinders: eight, five, four, six, three, twelve, two.
   17. engine-size: continuous from 61 to 326.
   18. fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi.
   19. bore: continuous from 2.54 to 3.94.
   20. stroke: continuous from 2.07 to 4.17.
   21. compression-ratio: continuous from 7 to 23.
   22. horsepower: continuous from 48 to 288.
   23. peak-rpm: continuous from 4150 to 6600.
   24. city-mpg: continuous from 13 to 49.
   25. highway-mpg: continuous from 16 to 54.
   26. price: continuous from 5118 to 45400.

8. Missing Attribute Values: (denoted by "?") Attribute #: Number of instances missing a value:

   1. 41
   2. 2
   3. 4
   4. 4
   5. 2
   6. 2
   7. 4

The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.This service contains all wildland fire incidents from the IRWIN (Integrated Reporting of Wildland Fire Information) incident service that meet the following criteria:Categorized as a Wildfire (WF), Prescribed Fire (RX), or Incident Complex (CX) recordIs Valid and not "quarantined" in IRWIN due to potential conflicts with other recordsNo "fall-off" rules are applied to this service.The date range for this service will extend from present day back to 2014, when IRWIN was implemented.Criteria were determined by an NWCG Geospatial Subcommittee task group. Data are refreshed from IRWIN source every 5 minutes.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes:SourceOIDThe OBJECTID value of the source record in the source dataset providing the attribution.ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency IA fire suppression on A, B, C & D size class fires on FS lands.ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.ContainmentDateTimeThe date and time a wildfire was declared contained.ControlDateTimeThe date and time a wildfire was declared under control.CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.IncidentSizeReported for a fire. The minimum size is 0.1.DiscoveryAcresAn estimate of acres burning when the fire is first reported by the first person to call in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.EstimatedCostToDateThe total estimated cost of the incident to date.FinalAcresReported final acreage of incident.FinalFireReportApprovedByTitleThe title of the person that approved the final fire report for the incident.FinalFireReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.FinalFireReportApprovedDateThe date that the final fire report was approved for the incident.FireBehaviorGeneralA general category describing how the fire is currently reacting to the influences of fuel, weather, and topography.FireBehaviorGeneral1A more specific category further describing the general fire behavior (how the fire is currently reacting to the influences of fuel, weather, and topography).FireBehaviorGeneral2A more specific category further describing the general fire behavior (how the fire is currently reacting to the influences of fuel, weather, and topography). FireBehaviorGeneral3A more specific category further describing the general fire behavior (how the fire is currently reacting to the influences of fuel, weather, and topography).FireCauseBroad classification of the reason the fire occurred identified as human, natural or unknown. FireCauseGeneralAgency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. For statistical purposes, fire causes are further broken into specific causes. FireCauseSpecificA further categorization of each General Fire Cause to indicate more specifically the agency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. FireOutDateTimeThe date and time when a fire is declared out. FireStrategyConfinePercentIndicates the percentage of the incident area where the fire suppression strategy of "Confine" is being implemented.FireStrategyFullSuppPercentIndicates the percentage of the incident area where the fire suppression strategy of "Full Suppression" is being implemented.FireStrategyMonitorPercentIndicates the percentage of the incident area where the fire suppression strategy of "Monitor" is being implemented.FireStrategyPointZonePercentIndicates the percentage of the incident area where the fire suppression strategy of "Point Zone Protection" is being implemented.FSJobCodeSpecific to the Forest Service, code use to indicate the FS job accounting code for the incident. Usually displayed as 2 char prefix on FireCode.FSOverrideCodeSpecific to the Forest Service, code used to indicate the FS override code for the incident. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.GACC"A code that identifies the wildland fire geographic area coordination center (GACC) at the point of origin for the incident. A GACC is a facility used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic area."ICS209ReportDateTimeThe date and time of the latest approved ICS-209 report.ICS209ReportForTimePeriodFromThe date and time of the beginning of the time period for the current ICS-209 submission.ICS209ReportForTimePeriodToThe date and time of the end of the time period for the current ICS-209 submission. ICS209ReportStatusThe version of the ICS-209 report (initial, update, or final). There should never be more than one initial report, but there can be numerous updates and multiple finals (as determined by business rules).IncidentManagementOrganizationThe incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.IncidentNameThe name assigned to an incident.IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category breaks down the Event Kind into more specific event categories.IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.InitialLatitudeThe latitude of the initial reported point of origin specified in decimal degrees.InitialLongitudeThe longitude of the initial reported point of origin specified in decimal degrees.InitialResponseAcresAn estimate of acres burning at the time of initial response (when the IC arrives and performs initial size up) The minimum size must be 0.1. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.InitialResponseDateTimeThe date/time of the initial response to the incident (when the IC arrives and performs initial size up)IrwinIDUnique identifier assigned to each incident record in IRWIN.IsFireCauseInvestigatedIndicates if an investigation is underway or was completed to determine the cause of a fire.IsFSAssistedIndicates if the Forest Service provided assistance on an incident outside their jurisdiction.IsMultiJurisdictionalIndicates if the incident covers multiple jurisdictions.IsReimbursableIndicates the cost of an incident may be another agency’s responsibility.IsTrespassIndicates if the incident is a trespass claim or if a bill will be pursued.IsUnifiedCommandIndicates whether the incident is being managed under Unified Command. Unified Command is an application of the ICS used when there is more than one agency with incident jurisdiction or when incidents cross political jurisdictions. Under Unified Command, agencies work together through their designated IC at a single incident command post to establish common objectives and issue a single Incident Action Plan.LocalIncidentIdentifierA number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year.ModifiedBySystemArcGIS Server username of system that last modified the IRWIN Incident record.PercentContainedIndicates the percent of incident area that is no longer active. Reference definition in fire line handbook when developing standard.PercentPerimeterToBeContainedIndicates the percent of perimeter left to be completed. This entry is appropriate for full suppression, point/zone protection, and confine fires, or any combination of these strategies. This entry is not used for wildfires managed entirely under a monitor strategy. (Note: Value is not currently being passed by ICS-209)POOCityThe closest city to the incident point of origin.POOCountyThe County Name identifying the county or equivalent entity at point of origin designated at the time of collection.POODispatchCenterIDA unique identifier for the dispatch center that intersects with the incident point of origin.POOFipsThe code which uniquely identifies

Large white, Grade A chicken eggs, sold in a carton of a dozen. Includes organic, non-organic, cage free, free range, and traditional."

UPDATED EVERY WEEK Last Update - 26th July 2025

Disclaimer!!! Data uploaded here are collected from the internet and some google drive. The sole purposes of uploading these data are to provide this Kaggle community with a good source of data for analysis and research. I don't own these datasets and am also not responsible for them legally by any means. I am not charging anything (either money or any favor) for this dataset. RESEARCH PURPOSE ONLY

Context

• The NIFTY 50 is a well-diversified 50 stock index and it represents 13 important sectors of the economy.
• It is used for a variety of purposes such as benchmarking fund portfolios, index-based derivatives, and index funds.
• NIFTY 50 is owned and managed by NSE Indices Limited.
• The NIFTY 50 index has shaped up to be the largest single financial product in India.

This data contains all the indices of NSE. NIFTY 50, NIFTY BANK, NIFTY 100, NIFTY COMMODITIES, NIFTY CONSUMPTION, NIFTY FIN SERVICE, NIFTY IT, NIFTY INFRA, NIFTY ENERGY, NIFTY FMCG, NIFTY AUTO, NIFTY 200, NIFTY ALPHA 50, NIFTY 500, NIFTY CPSE, NIFTY GS COMPSITE, NIFTY HEALTHCARE, NIFTY CONSR DURBL, NIFTY LARGEMID250, NIFTY INDIA MFG, NIFTY IND DIGITAL, INDIA VIX

File Information and Column Descriptions.

Nifty 50 index data with 1 minute data. The dataset contains OHLC (Open, High, Low, and Close) prices from Jan 2015 to Aug 2024. - This dataset can be used for time series analysis, regression problems, and time series forecasting both for one step and multi-step ahead in the future. - Options data can be integrated with this minute data, to get more insight about this data. - Different backtesting strategies can be built on this data.

File Information

• This dataset contains 6 files, each file contains nifty 50 data with different intervals.
• Different intervals are - 1 min, 3 min, 5 min, 15 min, and 1 hour, Daily data from intervals of 2015 Jan to 2024 August.

Column Descriptors

• Each file contains OHLC (Open, High, Low, and Close) prices and Data time information. Since these are Nifty 50 index data, so volume is not present.

Inspiration

Time series forecasting - Predict stock price

• Predict future stock price one step ahead and multi-step ahead in time.
• Use different time series forecasting techniques for forecasting the future stock price. ### Machine learning and Deep learning techniques
• Possible ML and DL models include Neural networks, RNNs, LSTMs, Transformers, Attention networks, etc.
• Different error functions can be considered like RMSE, MAE, RMSEP etc. ### Feature engineering
• Different augmented features can be created and that can be used for forecasting.
• Correlation analysis, Feature importance to justify the important features.

This dataset include some onchain indicator information at daily basis for Bitcoin. This informations:

• exchange onchain market depth
• inflow transaction count
• inflow volume
• netflows
• open interest to market cap ratio
• outflow transaction count
• outflow volume
• telegram sentiment
• total flows
• trades per side
• twitter sentiment

Inflow Volume

IntoTheBlock has built a proprietary machine learning powered classifier to identify addresses of top centralized exchanges, including their deposit addresses, withdrawal addresses, hot wallets and cold wallets. With this classifier, IntoTheBlock can measure the total amount of a given crypto-asset flowing into exchanges and measures this in dollar and crypto terms. The result is the Inflow Volume indicator.

Outlow Volume

While Inflow Volume at times anticipate volatility, Outflow Volume is often more reactive. In other words, Outflow Volume often spikes following either a crash or a significant break-out as shown in the example above. This could potentially be interpreted as users going long and opting to hold their crypto outside centralized exchanges.

Total Flows IntoTheBlock uses machine learning algorithms to identify centralized exchanges’ deposit and withdrawal addresses. Through this process, IntoTheBlock measures the total activity flowing in and out of centralized exchanges. The result is the Total Flows indicator which is measured the following way

Total Flows = Inflow Volume + Outlow Volume

Net Flows

The Net Flows indicator highlights trends of traders sending money in and out of exchanges. Recall that Net Flows are positive when more funds are entering than leaving exchanges. Therefore, we observe that positive Net Flows tend to coincide with periods following large increases in price (like LINK when it tripled between April and July) or confirmation of down-trends (as seen with LINK in late August).

Conversely, Net Flows are negative when a greater volume is being withdrawn from exchanges. This could be seen as a sign of accumulation (LINK in early August) or addresses buying back following large declines (LINK in early September).

While Net Flows also affect large cap crypto-assets, smaller cap tokens are more susceptible to large changes in prices deriving from exchange flows. This is simply a result of smaller caps requiring less capital in order to make market-moving trades. This is worth considering when using the Net Flows indicator to trade.

Net Flows = Inflow Volume - Outflow Volume

Outflow Transaction Count

The Outflow Transaction Count indicator provides indication of users withdrawing their funds from centralized exchanges likely to store in safer cold wallets. This is a valuable approximation of users going long and opting to hold their own funds. For this reason, outflows tend to spike as price crashes as pointed in the example above. While this can be the case on several occasions, natural fluctuations in exchanges’ flows can often have smaller spikes without regards to price action as well.

Inflow Transaction Count

As the name suggests, the Inflow Transaction Count indicator provides the number of incoming crypto transactions entering exchanges. While the Inflow Volume measures the aggregate dollar amount, which is influenced by whales’ transactions, the Inflow Transaction Count is a better approximation of the number of users sending funds into exchanges.

This indicator has also shown to rise along and anticipate periods of high volatility. For example, on September 1st, inflow transactions for Bitcoin hit a 3-month high preceding a decrease in price of 14% over the following 48 hours. While this pattern does tend to emerge, natural fluctuations in inflow transactions can also increase at times.