Russia Export Unit Value Index: Machines, Equipment & Transportation Means data was reported at 113.600 Prev Year=100 in 2017. This records an increase from the previous number of 85.500 Prev Year=100 for 2016. Russia Export Unit Value Index: Machines, Equipment & Transportation Means data is updated yearly, averaging 98.300 Prev Year=100 from Dec 1999 (Median) to 2017, with 19 observations. The data reached an all-time high of 118.800 Prev Year=100 in 2011 and a record low of 84.900 Prev Year=100 in 2000. Russia Export Unit Value Index: Machines, Equipment & Transportation Means data remains active status in CEIC and is reported by Federal Customs Service. The data is categorized under Russia Premium Database’s Foreign Trade – Table RU.JAF010: Export and Import: Unit Value Index: by Product: Annual.

Baltic Dry rose to 2,600 Index Points on December 2, 2025, up 0.66% from the previous day. Over the past month, Baltic Dry's price has risen 33.68%, and is up 110.19% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on December of 2025.

Russia Export Unit Value Index: CIS: Machines, Equipment & Transportation Means data was reported at 109.300 Prev Year=100 in 2017. This records an increase from the previous number of 97.000 Prev Year=100 for 2016. Russia Export Unit Value Index: CIS: Machines, Equipment & Transportation Means data is updated yearly, averaging 105.300 Prev Year=100 from Dec 1999 (Median) to 2017, with 19 observations. The data reached an all-time high of 125.000 Prev Year=100 in 2008 and a record low of 70.100 Prev Year=100 in 1999. Russia Export Unit Value Index: CIS: Machines, Equipment & Transportation Means data remains active status in CEIC and is reported by Federal Customs Service. The data is categorized under Russia Premium Database’s Foreign Trade – Table RU.JAF014: Export and Import: Unit Value Index: by Product: CIS Countries: Annual.

Unit-values are defined by: trade value / quantity. These unit-values are divided by the average unit-value of the previous year to obtain elementary unit-value indices, from which outliers are detected and removed. Elementary unit-value indices are then aggregated over countries and commodities, by using formulae. Finally, the unit-value indices are chained back to the reference year (2015 = 100) and used to approximate the import and export price movements.

This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains UV aerosol index values. The data are averaged in 5 degree latitude bands. These data are stored in an ASCII format.The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).

Russia Import Unit Value Index: Non CIS: Machines, Equipment & Transportation Means data was reported at 107.400 Prev Year=100 in 2017. This records an increase from the previous number of 96.700 Prev Year=100 for 2016. Russia Import Unit Value Index: Non CIS: Machines, Equipment & Transportation Means data is updated yearly, averaging 98.800 Prev Year=100 from Dec 1999 (Median) to 2017, with 19 observations. The data reached an all-time high of 116.300 Prev Year=100 in 2008 and a record low of 71.800 Prev Year=100 in 2000. Russia Import Unit Value Index: Non CIS: Machines, Equipment & Transportation Means data remains active status in CEIC and is reported by Federal Customs Service. The data is categorized under Russia Premium Database’s Foreign Trade – Table RU.JAF012: Export and Import: Unit Value Index: by Product: Non CIS Countries: Annual.

Redbook Index in the United States increased by 7.60 percent in the week ending November 29 of 2025 over the same week in the previous year. This dataset provides the latest reported value for - United States Redbook Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

🇩🇪 독일 English The data set "UV Index" provides information on the radiation exposure of ultraviolet radiation (UV) in the city of Münster. The UV index is a standard measure of the strength of sunburn-producing ultraviolet radiation at a specific location and time. Main characteristics of the dataset: Data type: Numerical values representing the UV index. A value of 0 means "no load", values greater than 11 mean "extreme load". The dataset is updated daily to reflect the current UV radiation values. Available in CSV and JSON formats for analysis and integration into applications. Data source: German Weather Service (DWD)

The IMF-adapted ND-GAIN index is an adaptation of the original index, adjusted by IMF staff to replace the Doing Business (DB) Index, used as source data in the original ND-GAIN, because the DB database has been discontinued by the World Bank in 2020 and it is no longer allowed in IMF work. The IMF-adapted ND-GAIN is an interim solution offered by IMF staff until the ND-GAIN compilers will review the methodology and replace the DB index.Sources: ND-GAIN; Findex - The Global Findex Database 2021; Worldwide Governance Indicators; IMF staff calculations. Category: AdaptationData series: IMF-Adapted ND-GAIN IndexIMF-Adapted Readiness scoreReadiness score, GovernanceReadiness score, IMF-Adapted EconomicReadiness score, SocialVulnerability scoreVulnerability score, CapacityVulnerability score, EcosystemsVulnerability score, ExposureVulnerability score, FoodVulnerability score, HabitatVulnerability score, HeathVulnerability score, SensitivityVulnerability score, WaterVulnerability score, InfrastructureMetadata:The IMF-adapted ND-GAIN Country Index uses 75 data sources to form 45 core indicators that reflect the vulnerability and readiness of 192 countries from 2015 to 2021. As the original indicator, a country's IMF-adapted ND-GAIN score is composed of a Readiness score and a Vulnerability score. The Readiness score is measured using three sub-components – Economic, Governance and Social. In the original ND-GAIN database, the Economic score is built on the DB index, while in the IMF-adapted ND-GAIN, the DB Index is replaced with a composite index built using the arithmetic mean of “Borrowed from a financial institution (% age 15+)” from The Global Financial Index database (FINDEX_BFI) and “Government effectiveness” from the Worldwide Governance Indicators database (WGI_GE). The Vulnerability, Social and Governance scores do not contain any DB inputs and, hence, have been sourced from the original ND-GAIN database. Methodology:The procedure for data conversion to index is the same as the original ND-GAIN and follows three steps: Step 1. Select and collect data from the sources (called “raw” data), or compute indicators from underlying data. Some data errors (i.e., tabulation errors coming from the source) are identified and corrected at this stage. If some form of transformation is needed (e.g., expressing the measure in appropriate units, log transformation to better represent the real sensitivity of the measure etc.) it happens also at this stage. Step 2. At times some years of data could be missing for one or more countries; sometimes, all years of data are missing for a country. In the first instance, linear interpolation is adopted to make up for the missing data. In the second instance, the indicator is labeled as "missing" for that country, which means the indicator will not be considered in the averaging process. Step 3. This step can be carried out after of before Step 2 above. Select baseline minimum and maximum values for the raw data. These encompass all or most of the observed range of values across countries, but in some cases the distribution of the observed raw data is highly skewed. In this case, ND-GAIN selects the 90-percentile value if the distribution is right skewed, or 10-percentile value if the distribution is left skewed, as the baseline maximum or minimum. Based on this procedure, the IMF–Adapted ND-GAIN Index is derived as follows: i. Replace the original Economic score with a composite index based on the average of WGI_GE and cubic root of FINDEX_BFI1, as follows:IMF-Adapted Economic = ½ · (WGI_GE) + ½ · (FINDEX_BFI)1/3 (1) The IMF-adapted Readiness and overall IMF-adapted ND-GAIN scores are then derived as: IMF-Adapted ND-GAIN Readiness = 1/3 · ( IMF-Adapted Economic + Governance + Social) IMF-Adapted ND-GAIN = ½·( IMF-Adapted ND-GAIN Readiness+ND-GAIN Vulnerability) ii. In case of missing data for one of the indicators in (1), IMF-Adapted ND-GAIN Economic would be based on the value of the available indicator. In case none of the two indicators is available, the IMF-Adapted Economic score would not be produced but the IMF-Adapted ND-GAIN Readiness would be computed as average of the Governance and Social scores. This approach, that replicates the approach used to derive the original ND-GAIN indexes in case of missing data, ensures that the proposed indicator has the same coverage as the original ND-GAIN database.
1 Given that the FINDEX_BFI data are positively skewed, a cubic root transformation has been implemented to induce symmetry.

China Trade Index: MoM: Unit Value: Import HS4: Carbides, Whether or not Chemically Defined data was reported at 101.400 Average 12 Mths PY=100 in Feb 2025. This records a decrease from the previous number of 114.000 Average 12 Mths PY=100 for Jan 2025. China Trade Index: MoM: Unit Value: Import HS4: Carbides, Whether or not Chemically Defined data is updated monthly, averaging 108.200 Average 12 Mths PY=100 from Jan 2018 (Median) to Feb 2025, with 85 observations. The data reached an all-time high of 219.200 Average 12 Mths PY=100 in Apr 2020 and a record low of 56.492 Average 12 Mths PY=100 in Sep 2024. China Trade Index: MoM: Unit Value: Import HS4: Carbides, Whether or not Chemically Defined data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Unit Value Index: MoM: HS4 Classification.

This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains UV aerosol index values. The data are averaged in 5 degree latitude bands. These data are stored in an ASCII format.The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).

China Trade Index: MoM: Unit Value: Import HS4: Sulphides; Polysulphides, Whether or not Chemically Defined data was reported at 191.300 Average 12 Mths PY=100 in Feb 2025. This records an increase from the previous number of 106.100 Average 12 Mths PY=100 for Jan 2025. China Trade Index: MoM: Unit Value: Import HS4: Sulphides; Polysulphides, Whether or not Chemically Defined data is updated monthly, averaging 111.937 Average 12 Mths PY=100 from Jan 2018 (Median) to Feb 2025, with 85 observations. The data reached an all-time high of 207.600 Average 12 Mths PY=100 in Feb 2023 and a record low of 62.421 Average 12 Mths PY=100 in Jul 2023. China Trade Index: MoM: Unit Value: Import HS4: Sulphides; Polysulphides, Whether or not Chemically Defined data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Unit Value Index: MoM: HS4 Classification.

Values of CK in (17), in (21) and (26), d* in (26), HHI defined in Table 1, and CKH in (25) for a sample of real market-share data.

Values of CK in (17), in (21) and (26), d* in (26), HHI and CR4 defined in Table 1, and CKH in (25) for randomly generated Pn = (p1,…,pn) and 2≤n≤30.

Context

Time series modelling for the prediction of stocks prices is a challenging task. Political events, market expectations and economic factors are just a few known factors that can impact financial market behaviour. The financial market is a complex, noisy, evolutionary and chaotic field of study that attracts many enthusiasts and researches — the first, usually driven by the economic benefit of it, the latter, inspired by the challenge of handling such complex data.

This project aims to predict Facebook (FB) next day stock price direction with machine learning algorithms. Technical indicators and global market indexes are used, and their influence on the forecast accuracy is analysed.

Content

Daily values were retrieved (volume, open, close, low and high prices) from Yahoo! Finance website. For Facebook (FB), July 2012 was the earliest data available. The date range is July 2012 to November 2018.

The closing price of current day C(t) and closing price from the previous day C(t-1) are compared to build the initial dataset. The objective is to define if the price trend is going up or down by analysing these two values. For each instance, a comparison was made and recorded. If the price is going up, C(t) > C(t-1), class “1” is assigned. Class “0” is assigned for the opposite case.

• ID: Sample ID
• Close: Closing value of previous day
• Low: Lowest value of previous day
• High: Highest value of previous day
• Volume: Volume value of previous day

Research was initiated to understand which features could help the model to forecast the stock direction. Three main routes were found: Lag features, Technical Indicators and Global Market Indexes. Below is an explanation of each group of features.

Lag features are features that contain the closing price and direction of previous days and it is a common strategy for Time Series models. The following features were added:

• C(t-5): Closing price of 5 days before
• C(t-4): Closing price of 4 days before
• C(t-3): Closing price of 3 days before
• C(t-2): Closing price of 2 days before
• C_up_4: Output 1 if closing price went up 4 days ago
• C_up_3: Output 1 if closing price went up 3 days ago
• C_up_2: Output 1 if closing price went up 2 days ago
• C_up_1: Output 1 if closing price went up 1 day ago

Technical indicators are used by researches and financial market analysts to support stock market trend forecasting. Common indicators retrieved from the literature were selected and calculated for Facebook stock. Techical Indicators added:

• MA-10: Moving Average considering previous 10 days
• MA-5: Moving Average considering previous 5 days
• WMA-10: Weighted Moving Average considering previous 10 days
• SO: Stochastic Oscillator
• M: Momentum as the difference in closing price in a 10 days interval
• SSO: Slow Stochastic Oscillator
• EMA: Exponential Moving Average for a 10 day period
• MACD_Sline_9: MACD Signal Line for a 9 day period
• RSI: Relative Strength Index
• CCI: Commodity Channel Index
• ADO: Accumulation Distribution Oscillator

Technical indicators provide a suggestion of the stock price movement. Additional features were created for each technical indicator by analysing its daily value and assigning a class according to their meaning. Class “1” is given if the indicator numerical value suggests upper trend, class “0” for a downtrend. In other words, financial market analysis is performed at a simplistic level, in the attempt to translate what the continuous value means.

• MA-10>C: If MA-10 is higher than Closing price output 1
• MA-5>C: If MA-5 is higher than Closing price output 1
• WMA-10>C: If WMA-10 is higher than Closing price output 1
• SO>SOt-1: Output is 1 if SO current value is higher than previous day
• M>0: A positive momentum outputs 1
• SSO>SSOt-1: SSO current value is higher than previous day
• EMA>C: If EMA is higher than Closing price output 1
• MACD>MACDt-1: If MACD current value is higher than previous day output 1
• RSI70-30: If RSI is above 70, output 0. Values below 30 output is one. For values within this range it compares to previous day and outputs 1 if value has increased
• CCI200-200: Similar to RSI, but if threshold set for 200 and -200.
• ADO>ADOt-1: Output is 1 if ADO current value is higher than previous day

For a given country or region, the stock market index characterises the performance of its financial market and the overall local economy. For this reason, the same day performance of these markets could contribute to the machine learning model predictions. Six global indexes were added as features, with their closing direction as up or down, class “1” or “0”, respectively. Data for these indexes (Nikkei, Hang Seng, All Ordinaries, Euronext 100, SSE and DAX) were also retrieved from Yahoo! Finance.

China Trade Index: YoY: Unit Value: Import HS4: Narrow Woven Fabrics of Textile Materials, With a Width of ≤ 30 Cm (Excluding Labels, Badges and Similar Articles); Narrow Fabrics Consisting of Warp Without Weft Assembled by Means of an Adhesive Bolducs'' data was reported at 105.500 Prev Year=100 in Mar 2025. This records an increase from the previous number of 91.200 Prev Year=100 for Feb 2025. China Trade Index: YoY: Unit Value: Import HS4: Narrow Woven Fabrics of Textile Materials, With a Width of ≤ 30 Cm (Excluding Labels, Badges and Similar Articles); Narrow Fabrics Consisting of Warp Without Weft Assembled by Means of an Adhesive Bolducs'' data is updated monthly, averaging 104.622 Prev Year=100 from Jan 2018 (Median) to Mar 2025, with 86 observations. The data reached an all-time high of 134.548 Prev Year=100 in Jan 2023 and a record low of 69.983 Prev Year=100 in Mar 2019. China Trade Index: YoY: Unit Value: Import HS4: Narrow Woven Fabrics of Textile Materials, With a Width of ≤ 30 Cm (Excluding Labels, Badges and Similar Articles); Narrow Fabrics Consisting of Warp Without Weft Assembled by Means of an Adhesive Bolducs'' data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Unit Value Index: YoY: HS4 Classification.

China Trade Index: YoY: Unit Value: Export HS4: Carbides, Whether or not Chemically Defined data was reported at 84.600 Prev Year=100 in Mar 2025. This records a decrease from the previous number of 106.200 Prev Year=100 for Feb 2025. China Trade Index: YoY: Unit Value: Export HS4: Carbides, Whether or not Chemically Defined data is updated monthly, averaging 101.000 Prev Year=100 from Jan 2018 (Median) to Mar 2025, with 86 observations. The data reached an all-time high of 154.300 Prev Year=100 in Mar 2022 and a record low of 72.800 Prev Year=100 in Nov 2024. China Trade Index: YoY: Unit Value: Export HS4: Carbides, Whether or not Chemically Defined data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Unit Value Index: YoY: HS4 Classification.

China Trade Index: YoY: Unit Value: Export HS4: Sulphides; Polysulphides, Whether or not Chemically Defined data was reported at 99.200 Prev Year=100 in Mar 2025. This records a decrease from the previous number of 110.800 Prev Year=100 for Feb 2025. China Trade Index: YoY: Unit Value: Export HS4: Sulphides; Polysulphides, Whether or not Chemically Defined data is updated monthly, averaging 98.966 Prev Year=100 from Jan 2018 (Median) to Mar 2025, with 86 observations. The data reached an all-time high of 172.300 Prev Year=100 in Jun 2022 and a record low of 74.700 Prev Year=100 in Mar 2024. China Trade Index: YoY: Unit Value: Export HS4: Sulphides; Polysulphides, Whether or not Chemically Defined data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Unit Value Index: YoY: HS4 Classification.

China Trade Index: YoY: Unit Value: Import HS4: Sulphides; Polysulphides, Whether or not Chemically Defined data was reported at 103.800 Prev Year=100 in Mar 2025. This records an increase from the previous number of 89.600 Prev Year=100 for Feb 2025. China Trade Index: YoY: Unit Value: Import HS4: Sulphides; Polysulphides, Whether or not Chemically Defined data is updated monthly, averaging 105.310 Prev Year=100 from Jan 2018 (Median) to Mar 2025, with 86 observations. The data reached an all-time high of 217.300 Prev Year=100 in Feb 2023 and a record low of 64.454 Prev Year=100 in Jan 2019. China Trade Index: YoY: Unit Value: Import HS4: Sulphides; Polysulphides, Whether or not Chemically Defined data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Unit Value Index: YoY: HS4 Classification.

China EVI: MoM: HS4: Carbides, Whether or not Chemically Defined data was reported at 55.400 Average 12 Mths PY=100 in Feb 2025. This records a decrease from the previous number of 93.000 Average 12 Mths PY=100 for Jan 2025. China EVI: MoM: HS4: Carbides, Whether or not Chemically Defined data is updated monthly, averaging 95.300 Average 12 Mths PY=100 from Jan 2018 (Median) to Feb 2025, with 85 observations. The data reached an all-time high of 364.500 Average 12 Mths PY=100 in Mar 2020 and a record low of 55.400 Average 12 Mths PY=100 in Feb 2025. China EVI: MoM: HS4: Carbides, Whether or not Chemically Defined data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Trade Value Index: MoM: HS4 Classification.