10 Best Datasets for Time Series Analysis

Time series data is essentially a collection of data points that are organized in time. Time is frequently the independent variable, and the purpose is usually to forecast the future in time series.

In this article, we will look at the 10 Best Datasets for Time Series Analysis, in order to understand how a certain metric varies over time and to predict future values.

List of the Best Time Series Datasets

1. M4 Competition Dataset - This dataset is a collection of over 100,000 time series of yearly, quarterly, monthly and other (weekly, daily and hourly) data, divided into training and test datasets and is used in the M4 Forecasting Competition, which is an annual competition organized by the International Institute of Forecasters (IIF). The competition aims to evaluate and compare different forecasting methods and models on a diverse set of time series.

   
   

2. Numenta Anomaly Benchmark (NAB) - The NAB dataset is a collection of real-world and artificial time series data, and is used to evaluate algorithms for anomaly detection. It comprises diverse time series data that cover different domains such as finance, transportation, and environmental monitoring, making it a suitable benchmark dataset for evaluating anomaly detection algorithms.

   
   

3. The U.S. Census Bureau's International Data Base (IDB) - The IDB dataset includes a variety of demographic and economic time series data for countries around the world. The dataset includes a variety of time series data for countries around the world, such as population, fertility, mortality, migration, and economic indicators, collected and provided by the U.S. Census Bureau, which is an independent agency of the U.S. Federal Government.

   
   

4. The Google Trends Dataset - This dataset contains the relative popularity of a particular search term over time, as measured by Google. It is also based on the number of Google searches for a particular term, normalized to reflect the popularity of the term relative to the total number of searches on Google.

   
   

5. The New York City Taxi Dataset - This dataset includes time series data on taxi rides in New York City, including the pickup and drop-off times, locations, transportation prices, the distance of the trips, the fare, and the number of passengers.

   
   

6. The Beijing Multi-Site Air-Quality Dataset - This dataset includes hourly measurements of air quality at various sites in Beijing.

   
   

7. The Wikipedia Web Traffic Time Series Dataset -This dataset includes time series data on the traffic to various Wikipedia articles. The dataset provides information on the number of views and visitors for each Wikipedia article, as well as the number of edits and contributors.

   
   

8. The Global Land and Ocean-Temperature Anomaly Time Series - This dataset includes time series data on the land and ocean surface temperature. The data is presented as temperature anomalies, which are the difference between the temperature of a given year and the average temperature from a reference period.

   
   

9. Monthly Sunspot Dataset - This dataset includes a monthly count of sunspots from 1749 to 2018 (269 years). Sunspots are colder areas of the sun's surface created by huge variations in the magnetic field of the sun. These variations in the sun's magnetic field can cause solar flares and coronal mass ejections, which can have significant effects on the Earth's atmosphere and climate.

   
   

10. Ozone Level Detection Dataset - This dataset includes two ground ozone level datasets used to detect the ozone level in the atmosphere. It was gathered in Houston, Galveston, and Brazoria areas between 1998 - 2004 and includes measurements of ozone levels on an hourly basis, where one is the eight-hour peak set (eighthr.data) and the other is the one-hour peak set (onehr.data).

Common Use Cases for Time Series Datasets

Time Series Analysis and Forecasting

• M4 Competition Dataset - The M4 Competition dataset is a standard for time series forecasting, thus researchers and practitioners frequently use it to assess their forecasting models and contrast them with state-of-the-art models.

  
  

• Numenta Anomaly Benchmark (NAB) - Researchers and practitioners use NAB to evaluate and compare the performance of different anomaly detection algorithms on a diverse set of time series data, and to test the robustness of these algorithms in different use cases.

  
  

• The Google Trends Dataset -  Researchers, marketers, and companies frequently utilize the Google Trends dataset to understand how search phrases and subjects change over time as well as to spot patterns and trends in online behaviour. This dataset can be used to track and comprehend changes in the level of popularity of a given brand, product, operations or issue. Additionally, it can be used to determine the most well-liked words and subjects in a particular language or region and contrast them with global trends.

  
  

  

  
  

• The Wikipedia Web Traffic Time Series Dataset - Researchers and data scientists frequently utilize the Wikipedia Web Traffic Time Series dataset to examine the dynamics of website traffic, comprehend how people interact with Wikipedia, and spot patterns and trends in online behaviour. It can be used to compare the traffic of various languages, examine the popularity of various Wikipedia articles, and follow the evolution of articles over time. It can also be used to comprehend how events, patterns, or other outside variables affect traffic.

  
  

• Monthly Sunspot Dataset - Solar physicists, meteorologists and climatologists frequently utilize the Monthly Sunspot dataset to research solar activity and how it affects the climate and atmosphere of the planet. The dataset can be used to study the relationships between solar activity and climate change, track the number of sunspots over time, and evaluate sunspot cycles. It can also be used to spot patterns and trends in solar activity and forecast solar activity in the future.

Demographic and Economic Analysis

• The U.S. Census Bureau's International Data Base (IDB) - The IDB dataset is particularly useful to researchers, policymakers, and businesses who study population dynamics, forecast future population growth, monitor economic development, and also compare the demographic and economic characteristics of different countries.

Environmental Monitoring

• The Beijing Multi-Site Air-Quality Dataset - Researchers, decision-makers and enterprises frequently utilize the Beijing Multi-Site Air-Quality dataset to comprehend Beijing's air quality and how it varies over time. It can be used to monitor the concentrations of various contaminants, spot patterns & trends in the air quality and comprehend how various variables like weather, traffic and industrial activity affect the air quality. It can also be applied to forecast future air quality and assess the efficacy of various air quality control techniques.

  
  

• The Global Land and Ocean-Temperature Anomaly Time Series - To examine global temperature trends and comprehend how the temperature is changing over time, climatologists, meteorologists and academics frequently use the Global Land and Ocean-Temperature Anomaly Time Series dataset. It can monitor temperature anomalies for both land and ocean surfaces, spot temperature patterns and trends, and comprehend how various elements like greenhouse gas emissions, solar activity, and ocean currents affect the temperature.

  
  

• Ozone Level Detection Dataset - Researchers, decision-makers, and companies frequently utilize the Ozone Level Detection dataset to comprehend the ozone levels in the atmosphere and how they alter over time. It can be used to monitor ozone levels, spot patterns and trends and comprehend how various elements like climate, traffic and industrial activity affect ozone levels.

  
  

Transportation

• The New York City Taxi Dataset - To comprehend the transportation patterns in New York City, researchers, urban planners, and enterprises frequently use the New York City Taxi dataset. It can be used to keep tabs on the number of rides, the distance covered, and the money the taxi business brings in. It can also be used to spot patterns and trends in transportation, such as peak travel times, well-travelled routes, and the relationship between weather and demand for transportation.

Final Thoughts on Time Series Datasets

Some of these datasets can be used across multiple categories, while others are not constrained to a single-use scenario.

They are also available for anyone to download and use freely.