Modal Service data and Safety & Security (S&S) public transit time series data delineated by transit/agency/mode/year/month. Includes all Full Reporters--transit agencies operating modes with more than 30 vehicles in maximum service--to the National Transit Database (NTD). This dataset will be updated monthly. The monthly ridership data is released one month after the month in which the service is provided. Records with null monthly service data reflect late reporting. The S&S statistics provided include both Major and Non-Major Events where applicable. Events occurring in the past three months are excluded from the corresponding monthly ridership rows in this dataset while they undergo validation. This dataset is the only NTD publication in which all Major and Non-Major S&S data are presented without any adjustment for historical continuity.

This dataset contains 366 monthly time series used in the Kaggle Tourism forecasting competition.

Historical Past (1895-1980) - Time series datasets prior to 1981 are modeled using climatologically-aided interpolation (CAI), which uses the long-term average pattern (i.e., the 30-year normals) as first-guess of the spatial pattern of climatic conditions for a given month or day. CAI is robust to wide variations in station data density, which is necessary when modeling long time series. Data is based on Monthly and Annual dataset covering the conterminous U.S. from 1981 to now. Contains spatially gridded monthly and annual total precipitation at 4km grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University.

Monthly estimate of gross domestic product (GDP) containing constant price gross value added (GVA) data for the UK.

This dataset contains 1428 monthly time series used in the M3 forecasting competition. The series are belonging to 6 different domains: demographic, micro, macro, industry, finance and other.

Dive into this dataset featuring monthly counts of airplane passengers, spanning 144 observations. Each entry records the passenger count for a specific month.

Variables:

Month: Month of the observation. Passengers: Number of passengers recorded for the corresponding month. Ideal for:

Exploring seasonal patterns and long-term trends in passenger traffic. Building predictive models to forecast future passenger counts. Understanding the impact of external factors on air travel demand. Perfect for learners and experts alike, this dataset offers a valuable opportunity to analyze and predict trends in airplane passenger traffic.

The gridded CRU TS (time-series) 3.22 data are month-by-month variations in climate over the period 1901-2013, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia.

CRU TS 3.22 variables are cloud cover, diurnal temperature range, frost day frequency, PET, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure and wet day frequency for the period Jan. 1901 - Dec. 2013.

CRU TS 3.22 data were produced using the same methodology as for the 3.21 datasets. In addition to updating the dataset with 2013 data, the v3.22 release corrects an error in the v3.21 dataset. This is summarised in the document, CRU_Advisory_v3.2x_NE_Africa.txt, and affects PRE and WET variables only. There are several known issues with the current dataset which cannot be resolved in the timeframe of this release; they will be addressed in the future. This directory also contains an advisory note regarding an issue with 35 Mozambique stations that were new. After an investigation by the CRU, the comparison plots show that the only countries affected in a possibly significant way are Egypt and Eritrea. The details of these can be found in this directory.

The CRU TS 3.22 data are monthly gridded fields based on monthly observational data, which are calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and netcdf data files both contain monthly mean values for the various parameters.

All CRU TS output files are actual values - NOT anomalies.

CRU TS data are available for download to all CEDA users. The CEDA Web Processing Service (WPS) may be used to extract a subset of the data (please see link to WPS below).

This is a supplementary material for the following research paper:

Tutun, S., Chou, C. A., & Canıyılmaz, E. (2015). A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey. Energy, 93, 2406-2422.

http://www.sciencedirect.com/science/article/pii/S0360544215014322

This dataset is the first collected electricity data as monthly data instead of annual data for forecasting of electricity energy in Turkey. The dataset of electricity in Turkey is used to forecast the net electricity consumption. The necessary data, including each variable and covering a period of 35 years (as monthly), is collected from the TEIAS (Turkish electricity transmission company). As independent factors, the transmitted energy, gross generation, imports, and exports, which have high efficiency. In order to make a better analysis of Turkey's situation, it is necessary to review indicators such as imports, gross generation, exports and transmitted energy. Finally, this dataset can be used efficiently for future energy forecasting analysis because it has more observations and preprocessed for forecasting.

The U.S. Census Bureau.s economic indicator surveys provide monthly and quarterly data that are timely, reliable, and offer comprehensive measures of the U.S. economy. These surveys produce a variety of statistics covering construction, housing, international trade, retail trade, wholesale trade, services and manufacturing. The survey data provide measures of economic activity that allow analysis of economic performance and inform business investment and policy decisions. Other data included, which are not considered principal economic indicators, are the Quarterly Summary of State & Local Taxes, Quarterly Survey of Public Pensions, and the Manufactured Homes Survey. For information on the reliability and use of the data, including important notes on estimation and sampling variance, seasonal adjustment, measures of sampling variability, and other information pertinent to the economic indicators, visit the individual programs' webpages - http://www.census.gov/cgi-bin/briefroom/BriefRm.

The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.09 data are month-by-month variations in climate over the period 1901-2024, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia and funded by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre.

The CRU TS4.09 variables are cloud cover, diurnal temperature range, frost day frequency, wet day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2024.

The CRU TS4.09 data were produced using angular-distance weighting (ADW) interpolation. All versions prior to 4.00 used triangulation routines in IDL. Please see the release notes for full details of this version update.

The CRU TS4.09 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999.

All CRU TS output files are actual values - NOT anomalies.

The “DSS Income Support Recipient – Monthly Time Series” publication is a monthly report, covering the Income Support payments of Age Pension, Disability Support Payment, ABSTUDY (Living Allowance), Austudy, Carer Payment, JobSeeker Payment, Special Benefit, Parenting Payment Partnered, Parenting Payment Single, Youth Allowance Other and Youth Allowance Student and Apprentice. The report includes payment recipient numbers by demographics such as age, gender, state, earnings, rate and marital status.

Data are included from the GISS Surface Temperature (GISTEMP) analysis and the global component of Climate at a Glance (GCAG). Two datasets are provided: 1) global monthly mean and 2) annual mean te...

This dataset includes global mean surface temperature anomalies for each month from 1850 to 2024. The impacts of sea ice concentrations and an internal variability pattern on surface temperature anomalies are also available. Estimated temperature anomalies include these impacts. The internal variability pattern corresponds well to the El Niño Southern Oscillation. Results of sensitivity tests using alternate sea ice source datasets from the Japanese Meteorological Agency (COBE-SST3) and the National Snow and Ice Data Center (modified G10010v2 appended with G02202v4) are also available.

The maximum likelihood estimation approach allows for the estimated global mean surface temperature anomalies to be temporally complete for the entire time period. Other estimates of this dataset are also temporally complete.

The dataset below contains monthly sales data for trucks from a specific company throughout the year .

The dataset can be used to develop an ARIMA/SARIMA forecasting model for predicting the future sales

Do explore pinned 📌 notebook under code section for quick📊 reference

Consider an upvote ^ if you find the dataset useful

Monash Time Series Forecasting Repository which contains 30+ datasets of related time series for global forecasting research. This repository includes both real-world and competition time series datasets covering varied domains.

Based on the Multiple Analysis of Series for Homogenization (MASH) and CLIMATOL methods, we firstly detected, estimated, and adjusted inhomogeneities in daily mean temperature, maximum temperature, minimum temperature and precipitation at 366 stations in China from 1960 to 2021, then established homogenized daily temperature and precipitation data sets. All datasets are completed by cooperation between Greece (Laboratory of Atmospheric Physics, Department of Physics, University of Patras) and China (Key Laboratory of Regional Climate-Environment in Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences).The information are as following:Homogenized monthly and daily temperature and precipitation time series in China during 1960-2021.rar (118MB). There is a .xlsx file named Information at 366 stations in China, including four columns (No. Station No. Longitude, and Latitude) Four folders are named RR, TG, TN, and TX. In each folder there are two subfolders named daily and monthly. In each subfolder, there are 366 .csv files named with station No. In each .csv file, there are 2 columns (date and records). In updated version, the climate change indices dataset is deleted, because I will submit it again to SDB as an another dataset. In this version, the monthly and daily temperature and precipitation time series dataset is updated to 2021.

🇺🇸 English:

This dataset includes region-based water consumption records for daily and monthly intervals. It is suitable for time series forecasting models like Prophet and ARIMA.

🇹🇷 Türkçe:

Bu veri seti, bölge bazlı günlük ve aylık su tüketim verilerini içermektedir. Prophet ve ARIMA gibi zaman serisi tahmin modelleri için uygundur.

The Global Monthly and Seasonal Urban and Land Backscatter Time Series, 1993-2020, is a multi-sensor, multi-decadal, data set of global microwave backscatter, for 1993 to 2020. It assembles data from C-band sensors onboard the European Remote Sensing Satellites (ERS-1 and ERS-2) covering 1993-2000, Advanced Scatterometer (ASCAT) onboard EUMETSAT satellites for 2007-2020, and the Ku-band sensor onboard the QuikSCAT satellite for 1999-2009, onto a common spatial grid (0.05 degree latitude /longitude resolution) and time step (both monthly and seasonal). Data are provided for all land (except high latitudes and islands), and for urban grid cells, based on a specific masking that removes grid cells with > 50% open water or < 20% built land. The all-land data allows users to choose and evaluate other urban masks. There is an offset between C-band and Ku-band backscatter from both vegetated and urban surfaces that is not spatially constant. There is a strong linear correlation (overall R-squared value = 0.69) between 2015 ASCAT urban backscatter and a continental-scale gridded product of building volume, across 8,450 urban grid cells (0.05 degree resolution) from large cities in Europe, China, and the United States.

This is the first of two datasets containing derived data necessary to reproduce the results of the associated journal article: "On the Sensitivity of Annual Streamflow to Air Temperature." This first dataset contains basic basin characteristics of 2,673 gaged basins worldwide, along with associated monthly time series of basin-mean precipitation, air temperature, and net radiation. The streamflow data themselves are available directly from the Global Runoff Data Centre. From the inventory of discharge data holdings of the Global Runoff Data Centre, 2,673 stream gages were selected for which (1) at least 25 complete calendar years of monthly data overlapped in time with available climate data; (2) 500-m-resolution basin shapefiles were available from Global Runoff Data Centre; (3) the Global Reservoir and Dam database listed no artificial reservoirs located within the drainage basin; and (4) mean reported discharge did not exceed either of two estimates of basin precipitation by more than a factor of 10. Monthly precipitation data from the Global Precipitation Climatology Centre, both precipitation and temperature from University of East Anglia Climatic Research Unit, and net radiation data from NASA, all on 0.5-degree grids, were areally averaged over the basins.

Dataset

This dataset was created by Sachin Kaushik

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