What is Time Series Analysis?
Time plays an integral role in the field of data analytics. Time series analysis is one of the most common types of data analysis. It is a statistical technique focused on analyzing trends or events that happen in a time series or particular interval. When performing time series analysis, instead of recording random or intermittent data points, a Data Analyst records points of data that occur at regular intervals spread over a period of time. This valuable tool provides insights into how variables can change over time.
In order to successfully execute time series analysis, it’s important to work with a large set of data points for a representative sample size. This leads to more reliable and consistent results and helps guarantee that the patterns or trends that are discovered aren’t just outliers. In this form of analytics, time can serve as an independent variable used to make forecasts about the future. In addition, the data that’s collected can draw from past historical data to shed insights on what may still occur.
How is Time Series Analysis Used in Data Analytics?
Time series analysis has been used in various forms for thousands of years. This field of data analytics can be traced back to the ancients, who used it to record the movement of planets, as well as to navigate uncharted parts of the world. Today, time series analysis remains a valuable tool when working with non-stationary data such as elements that regularly fluctuate or change over time. Various industries, like retail, economics, and meteorology, all draw from time series analysis to help understand variables that change with time.
The field of time series analysis has many applications across industries. A time series may experience upward or downward trends, or might only fluctuate slightly around a central mean such as the body temperature in humans. Some time series pertain to single cycles, like daily blood pressure readings, or deal with a variety of cycles such as daily and yearly outdoor temperature patterns. Data Analysts who are trained to work with time series analysis provide valuable insights that help humans live healthier, safer, more productive lives.
This article will explore the various applications of time series analysis to the environment.
Studying the Environment with Time Series Analysis
Time series analysis has a plethora of applications for studying environmental occurrences, trends, and patterns. Here are a few of the ways it is currently being applied to environmental phenomena and concerns:
- Air pollution: Air quality affects all living beings on planet Earth. Poor air quality can lead to serious health problems, and even premature death, for individuals, especially those with respiratory problems. By studying data on air quality, it’s now possible to provide options to improve the quality of air around the globe. China is leading the efforts to improve air quality since they are one of the world leaders in air pollution. For years, their scientists and mathematicians have been gathering and analyzing huge stores of data to track, anticipate, and ultimately cut back on air pollution.
- Regular weather updates: Weather models can be used to recreate historical data, as well as to forecast what is yet to come. In the past few years, weather forecasting has become much more accurate thanks to the application of machine learning tools in the field of atmospheric science. These tools study the relationships between the available data and predictors and can offer very accurate predictions days, even weeks, out.
- Sunspots: Sunspots are lower-temperature concentrations on the Sun that appear to those on Earth as noticeably darker spots. They are caused by disturbances in the magnetic field of the Sun’s photosphere. Sunspot activity tends to follow a roughly eleven-year cycle, which is half of the Sun’s 22-year activity cycle. When Sunspots are active, an increased number of solar flares occur, which leads to more geomagnetic storm activity on Earth. These periods can also create problems with power grids and radio transmissions. By applying time series analysis to monitoring sunspot activity over time, it’s possible not only to observe what’s happening on the Sun in real-time, but to predict future Sun activities based on time progression and temperature fluctuation. This information makes it possible to prepare for times when power and the radio may be affected.
- Clouds: Harnessing thermoelectric energy has great potential to supply energy to countries around the globe. However, one of the main impediments blocking solar radiation is clouds. Applying short-term time series analysis to cloud forecasting can help provide valuable insights into when optimal energy supplies will be available. This form of analytics involves detecting cloud cover and classifying cloud types to determine when the Sun will be most obstructed from the clouds.
- Precipitation: In tropical countries that have two yearly seasons, a dry season and a rainy season, monitoring and predicting rainfall is of crucial importance, since those living there depend on this knowledge to know when to plant crops and bring in harvests. By comparing the value against time, it’s possible to know how much rain is expected to fall, and roughly when, so that arrangements can be made accordingly.
- Climate change: One of the main applications of time series analysis is using it to monitor the changes in climate occurring on Earth. Climate change is one of the most pressing problems currently facing humanity. Data from satellites and sensors provide information about Earth’s various ecosystems and help predict the effects of climate change in various sectors such as agriculture. By studying historical as well as real-time data, it’s now possible not only to mitigate some of the effects of global warming, but to develop more sustainable solutions to help combat the negative effects of increasing global temperatures.
In the future, it is likely that machine learning and artificial intelligence will play an even more important role when applying time series analysis to environmental data. By doing so, more accurate weather forecasts, temperature studies, and air quality reports will be available faster, providing insights that have the potential to improve the quality of life for those around the globe.
Hands-On Data Analytics Classes
If you’re interested in learning more about time series analysis, as well as the other methods for analyzing and visualizing data, Noble Desktop has you covered. Their data analytics classes are offered in New York City, as well as in the live online format in topics like Python, Excel, and SQL.
In addition, more than 130 live online data analytics courses are also available from top providers. Topics offered include FinTech, Excel for Business, and Tableau, among others. Courses range from three hours to six months and cost from $219 to $27,500.
Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.
For those searching for a data analytics class nearby, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse approximately 400 data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.