IntroductionThe scarcity of resources have affected food production, which has challenged the ability of Iran to provide adequate food for the population. Iterative and mounting sanctions on Iran by the international community have seriously eroded Iran's access to agricultural technology and resources to support a growing population. Limited moisture availability also affects Iran's agricultural production. The aim of this study was to analyze the influence of inflation, international sanctions, weather disturbances, and domestic crop production on the price of rice, wheat and lentils from 2010 to 2021 in Iran.MethodData were obtained from the statistical yearbooks of the Ministry of Agriculture in Iran, Statistical Center of Iran, and the Central Bank of Iran. We analyzed econometric measures of food prices, including CPI, food inflation, subsidy reform plan and sanctions to estimate economic relationships. After deflating the food prices through CPI and detrending the time series to resolve the non-linear issue, we used monthly Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation data to analyze the influence of weather disturbances on food prices.Results and discussionThe price of goods not only provides an important indicator of the balance between agricultural production and market demand, but also has strong impacts on food affordability and food security. This novel study used a combination of economic and climate factors to analyze the food prices in Iran. Our statistical modeling framework found that the monthly precipitation on domestic food prices, and ultimately food access, in the country is much less important than the international sanctions, lowering Iran's productive capability and negatively impacting its food security.

This dataset contains historical data on chili prices and weather conditions in Kota Singkawang. It includes monthly records of various chili prices, shallot and garlic prices, rainfall levels, number of rainy days, and inflation rates. This dataset is a cleaned and merged version of several publicly available datasets from Statistics Indonesia (BPS). See the attached README file for detailed sources and descriptions.

This Data is associated to the paper "PREDICTION OF FOOD COMMODITY PRICES IN KOTA SINGKAWANG USING MACHINE LEARNING: A COMPARATIVE STUDY OF RANDOM FOREST, LINEAR REGRESSION, AND XGBOOST" by Lestari, D. , Bangun, E., Gaol, F. and Matsuo, T.

Variance inflation factor values for multicollinearity of variables associated with income level in Nkongho-mbeng.

Indonesia Wholesale Price Index: Manufacturing: Manufacture of Rubber and Rubber’s Products: Rain Coat data was reported at 121.610 2018=100 in Dec 2023. This stayed constant from the previous number of 121.610 2018=100 for Nov 2023. Indonesia Wholesale Price Index: Manufacturing: Manufacture of Rubber and Rubber’s Products: Rain Coat data is updated monthly, averaging 117.380 2018=100 from Jan 2020 (Median) to Dec 2023, with 48 observations. The data reached an all-time high of 151.800 2018=100 in Dec 2022 and a record low of 112.120 2018=100 in Oct 2020. Indonesia Wholesale Price Index: Manufacturing: Manufacture of Rubber and Rubber’s Products: Rain Coat data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Inflation – Table ID.IB010: Wholesale Price Index: by Sector: Manufacturing (Discontinued).

India Retail Price Index: Industrial Workers: 2001p: Clothing, Bedding and Footwear: Rain Coat data was reported at 190.020 2001=100 in Oct 2018. This stayed constant from the previous number of 190.020 2001=100 for Sep 2018. India Retail Price Index: Industrial Workers: 2001p: Clothing, Bedding and Footwear: Rain Coat data is updated monthly, averaging 144.760 2001=100 from Jan 2006 (Median) to Oct 2018, with 154 observations. The data reached an all-time high of 190.020 2001=100 in Oct 2018 and a record low of 104.940 2001=100 in May 2006. India Retail Price Index: Industrial Workers: 2001p: Clothing, Bedding and Footwear: Rain Coat data remains active status in CEIC and is reported by Labour Bureau Government of India. The data is categorized under India Premium Database’s Inflation – Table IN.IG011: Retail Price Index: Industrial Workers: 2001=100: Clothing, Bedding and Footwear.

India Retail Price Index: Industrial Workers: 2016p: Rain Coat data was reported at 124.900 2016=100 in Feb 2025. This records an increase from the previous number of 124.700 2016=100 for Jan 2025. India Retail Price Index: Industrial Workers: 2016p: Rain Coat data is updated monthly, averaging 113.300 2016=100 from Sep 2020 (Median) to Feb 2025, with 54 observations. The data reached an all-time high of 124.900 2016=100 in Feb 2025 and a record low of 106.600 2016=100 in Dec 2020. India Retail Price Index: Industrial Workers: 2016p: Rain Coat data remains active status in CEIC and is reported by Labour Bureau. The data is categorized under India Premium Database’s Inflation – Table IN.IH022: Retail Price Index: Industrial Workers: 2016=100: Miscellaneous: Personal Care & Effects.

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.

Precipio Precipitation: Is PRPO Stock Ready to Soar?

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

The 2021 National Hydrologic Assessment offers an analysis of flood risk, water supply, and ice break-up and jam flooding for spring 2021 based on late summer, fall, and winter precipitation, frost depth, soil saturation levels, snowpack, current streamflow, and projected spring weather. NOAA's network of 122 Weather Forecast Offices, 13 River Forecast Centers, National Water Center, and other national centers nationwide assess this risk, summarized here at the national scale. Overall, a reduced risk of spring flooding exists this year primarily due to dry fall and winter, along with limited snow still remaining on the ground. Major flooding is not expected this spring season. Minor to moderate flooding is ongoing across portions of the Lower Missouri River Basin with the flood risk predicted to continue through spring. The exception to the reduced risk is over the Coastal Plain of the Carolinas and Lower Ohio River Basin where flooding is predicted this spring, driven by above normal precipitation over the winter months, which has led to ongoing elevated streamflows and flooding and highly saturated soil conditions. This wet pattern is expected to continue across the Coastal Plain of the Carolinas and Lower Ohio River Basin through spring. It is important to note that heavy rainfall at any time can lead to flooding, even in areas where overall risk is considered low. This assessment addresses only spring flood potential on the timescale of weeks to months, not days or hours. Debris flow and flash flooding often associated with burn scars and urban areas can form quickly and occur any time with heavy rainfall events. Nearly every day, flooding happens somewhere in the United States or its territories. Flooding can cause more damage than any other weather-related event...with an annual average direct damage impact of 8 billion dollars a year over the past 40 years, with these impact costs adjusted for inflation. Flooding is one of America's most underrated killers, causing nearly 100 fatalities per year… roughly half of which occur in vehicles. Flowing water can be particularly powerful and dangerous… with just six inches of water able to sweep a person off their feet… and two feet of rushing water able to carry a mid-size car downstream. No vehicle should ever attempt to cross a flooded roadway, and drivers are reminded to “Turn Around, Don’t Drown.” To be prepared, every American should know their flood risk and what to do before, during, and after a flood event. This information is available at www.ready.gov/floods. To remain apprised of your current flood risk, visit weather.gov for the latest official watches and warnings. For detailed hydrologic conditions and forecasts, go to water.weather.gov.

The Ahr and Erft floods of 2021: origin, characteristics and consequences: In July 2021, Germany and in particular the federal states of Rhineland-Palatinate and North Rhine-Westphalia were hit by the worst flooding events since the Hamburg storm surge of 1962. Over 180 people lost their lives, and many villages and cites are still scarred by the destruction several years after the disaster. In just 24 hours, beginning on the evening of July 13, 2021, at some stations of the German Meteorological Service rainfall was twice as high as the rainfall for an average July. According to studies by Trenczek et al. 2022a, based on official data from the German government, the flooding caused damage of over €40 billion in Germany alone, making it the costliest single event in post-war history in Germany. In a chronicle of the extreme weather damages that have occurred in Germany since 2000 compiled by Trenczek et al. (2022b), the July 2021 flood is responsible for over a quarter of all damages (including heat and drought events) and, adjusted for inflation, is around two times more costly than the Elbe flood in 2002.

Retail Price Index: Industrial Workers: 2001p: Weights: Clothing, Bedding and Footwear: Rain Coat在2018-10达0.000 %，相较于2018-09的0.000 %保持不变。Retail Price Index: Industrial Workers: 2001p: Weights: Clothing, Bedding and Footwear: Rain Coat数据按月度更新，2006-01至2018-10期间平均值为0.000 %，共154份观测结果。CEIC提供的Retail Price Index: Industrial Workers: 2001p: Weights: Clothing, Bedding and Footwear: Rain Coat数据处于定期更新的状态，数据来源于Labour Bureau Government of India，数据归类于India Premium Database的Inflation – Table IN.IG027: Retail Price Index: Industrial Workers: 2001=100: Weights: Clothing, Bedding and Footwear。

Retail Price Index: Industrial Workers: 2001p: Clothing, Bedding and Footwear: Rain Coat在2018-10达190.020 2001=100，相较于2018-09的190.020 2001=100保持不变。Retail Price Index: Industrial Workers: 2001p: Clothing, Bedding and Footwear: Rain Coat数据按月度更新，2006-01至2018-10期间平均值为144.760 2001=100，共154份观测结果。该数据的历史最高值出现于2018-10，达190.020 2001=100，而历史最低值则出现于2006-05，为104.940 2001=100。CEIC提供的Retail Price Index: Industrial Workers: 2001p: Clothing, Bedding and Footwear: Rain Coat数据处于定期更新的状态，数据来源于Labour Bureau Government of India，数据归类于India Premium Database的Inflation – Table IN.IG011: Retail Price Index: Industrial Workers: 2001=100: Clothing, Bedding and Footwear。