Time series analysis revolves around looking at data points in both the long and short term to see how time affects them. By looking at the past, you can then move on to construct linear models in which you forecast how these same data points might change in the future.

Time series generally rely on stochastic—or random—probability distributions. These distributions vary in a multitude of ways. Some are univariate while others are multivariate, for example. In addition, you’re likely to see both linear and nonlinear, as well as stationary and nonstationary time series.

Time series analysis contrasts with cross-sectional data analysis, in which you exclusively look at data at a specific point in time rather than over a sustained period. Professionals in various industries utilize algorithms in data science adjacent programming languages like Python and R to perform both types of analysis and gain as much insight into data as possible.

Once you complete a time series diagram, you make it far easier to complete trend analysis over a given time period. Here are just a few reasons time series analysis is useful:

• Allows for deeper insights: Time series analysis allows you to zoom in to look at specific data points as well as take a wide angle view of your information in the aggregate. For business and finance professionals, this makes it easier to notice outliers in your data, as opposed to more cyclical and predictable information you would expect from seasonality. Tracking regular time intervals grants you an ability to gain deeper insight into your past, present, and future.
• Assists in tracking data over time: Without using a time series, you’ll have a much harder time tracking data points over a significant period. Each point acts as a timestamp, along with any coefficients or additional data points that go along with it. These diagrams help you see all relevant information for an extended period, as well as any unexpected or intriguing deviations, in a useful way.
• Improves forecasting abilities: The more you understand about different points of past data, the greater your ability to forecast future values. By studying covariance, stationarity, and other key elements in your time series, you increase your ability to forecast accurately.

You can use different types of time series for different data analysis purposes. Consider these prominent time series models:

• Autoregressive models: In this type of time series dataset, you take an approach of linear regression in which each value depends on the previous one in some fashion. You can use related tools like an autocorrelation function to look even deeper under the hood of your dataset in a model like this.
• Integrated models: This time series model includes a series plot that can be either linear or nonlinear. You can improve your forecasting methods by utilizing tools like differencing and exponential smoothing to further hone your integrated models.
• Moving average models: This form of time series forecasting utilizes a stochastic, randomized data set. It differs from autoregressive and integrated models because of the increased propagation of future values it uses.

Autoregressive integrated moving average or ARIMA models use aspects from all three basic forms of time series analysis at the same time. The Box-Jenkins model is one of the most common ARIMA models you can use. By using the strengths of each model in a hybrid fashion, you gain more of an ability to depict your univariate or multivariate time series with as much accuracy and thoroughness as possible.

There are multiple different ways to put time series analysis to the test. These are just a few examples of common time series use cases:

• Constructing an earnings overview: Suppose a company wants to make sense of historical data about its quarterly earnings. There’s a seasonal component to this type of thing, but other factors might have affected profits over the same period of time. In a time series, you could extrapolate out unique anomalies over a period of time to start questioning why you pulled in more or less money as an organization.
• Following software development: In the world of open-source software, there are sometimes thousands of updates to written programs over time. The fluctuations in this sequence of data points serve as very useful time series inputs. For instance, as you look over the panel data, you can discover where and when a bug first appeared.
• Tracking stock prices: Imagine you study the stock market for a living. The longer you keep track of closing prices, the greater store of past values you have to draw from for a time series. These can help you predict future values, as well as any seasonal variation due to financial quarterly statements from the same companies. Financial firms sometimes use machine learning models and algorithms to comb through this sort of residual information, relying on AI (artificial intelligence) to excel in picking the right stocks.