Uses of Time Series Analysis in Data Analytics

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.

There are several different characteristics of time series that can be modeled to make accurate predictions:

• Autocorrelation pertains to the similarities between observations as a function of the gap of time between them.
• Seasonality is the term for periodic fluctuations, such as how energy consumption may be higher during the day than at night, or how online sales tend to go up before the holidays.
• Stationarity pertains to when a time series’ statistical properties remain constant over time. Stationary time series are the most desired for modeling; those that aren’t stationary often must be transformed so that they are stationary.

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 in 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.

Here are some of the most common industry uses of time series analysis:

• Time series analysis is a valuable tool for measuring societal trends like:
• Birth rate over time
• Migrational data
• Population
• Political trends
• When applied to the financial sector, time series analysis can measure:
• Stock prices
• Interest rates
• Quarterly sales
• Automated stock trading
• In the health sector, time series analysis can be used to monitor:
• Heart rate (EKG)
• Blood pressure
• Weight
• Brain activity (EEG)
• Cholesterol
• Real-time visualizations of vitals on medical devices
• When working with the environment, time series analysis can keep track of:
• Temperature fluctuations
• Rainfall and precipitation
• Weather fronts
• Cloud cover
• Humidity
• Global temperatures
• Sunspot activity
• Daily, hourly, or weekly weather updates
• Air pollution levels

Regardless of what is being measured, most time series analyses have three primary goals:

• Based on the values from a single time series, or from several series, future values can be forecast.
• The impact of one event can be assessed using time series analysis.
• Causal patterns that study the effects of variables instead of events can be studied using two or more time series.

Data Analysts who are skilled at working with time series data can offer their companies or organizations valuable insights based on past events. These observations are important because they can help Analysts predict events in the future that may financially impact the organization.

Drawbacks to Using Time Series Analysis

Although there are many benefits to applying time series analysis on data, there are some drawbacks to be aware of as well. Here are some of the most common challenges this form of analysis presents:

• When working with time series analysis, it can be difficult to generalize results from a single study.
• Problems can arise pertaining to accurately identifying the best model to represent data.
• It can be challenging to obtain appropriate measures for time series analysis.
• Attempting to understand the impact of one event using time series analysis can be difficult because other events are occurring simultaneously. Overlooked variables, in particular different events, can drastically impact results.
• Often, those performing time series analysis have to work with the least amount of data and yet still execute important decision-making.
• In studies that compare the effects of one variable to another, two time series are typically involved, one for the independent variable and one for the independent variable. However, problems can arise because the findings within each series are not completely independent of one another, which can result in a higher correlation between the two series than would be the case using standard formulas.
• When causal patterns in time series data are analyzed, the correlation present does not always mean there is causation.
• One of the most common problems faced when working with time series analysis pertains to forecasting. The time periods that are used to make predictions do not form a random sample.
• It can be impossible to know the true shape of the distribution at hand. Simply assuming a normal distribution is a simplification that can create real problems in time series work. For example, the 509-point drop of the Dow-Jones Industrial Average on one day in 1987 was so unexpected and unprecedented that it was largely impossible to forecast its occurrence using time series data from the prior four decades. In this instance, the history of the stock market was not useful for forecasting this plunge because the factor that affected the drop had not been present in prior historical examinations.

Hands-On Data Analytics Classes

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