# Understanding Time Series: Analyzing Data Trends Over Time

![Time Series](https://www.investopedia.com/thmb/IEG4YEY-j4PXx1U3TyyHU0YEq88=/1500x0/filters:no_upscale():max_bytes(150000):strip_icc()/TermDefinitions_TimeSeries__V1_CT-e4cb9fe23caf415491b1e850a1be300b.png)

*— Investopedia / Crea Taylor*

> **Definition**
>
> A time series is a data set that tracks a sample over time. It's often used in investment analysis for predicting trends and patterns. Specifically, statistical methods, such as ARIMA, are used in the analysis and forecasting.

## What Is a Time Series?

A time series is crucial for tracking data points like a security's price over a specific time and at regular intervals. By using time series analysis, investors gain insights into the factors that influence trends over time, whether in the short-term movement of a stock or the long-term shifts in economic indicators. This comprehensive analysis helps investors and analysts understand how assets or economic variables change and correlate with other factors over time.

### Key Takeaways

*   A time series is a sequence of data points collected or recorded at regular time intervals, often used in analyzing financial markets.
*   Time series analysis can identify patterns and trends over time, helping investors predict future market behaviors.
*   Techniques like the Box-Jenkins Model and ARIMA are common in time series forecasting for analyzing stock prices and economic indicators.
*   Cross-sectional data analysis differs from time series as it focuses on data from a single point in time, often used together in practice to enhance insight.
*   Time series data can suffer from autocorrelation, potentially biasing results, making careful analysis essential.  
    

## Exploring Time Series Applications and Benefits

A time series tracks changes in variables over time. In investments, it often tracks a security's price. This can be done short-term, like hourly prices in a day, or long-term, like monthly end-day prices over five years.

Time series analysis helps observe changes in assets or economic variables over time. It also allows comparison of these changes with other variables during the same period.

Time series is also used outside finance, like tracking population changes over time. The figure below depicts such a time series for the growth of the U.S. population over the century from 1900 to 2000.

![A time series graph of the population of the United States from 1900 to 2000.](https://www.investopedia.com/thmb/ZfsljZ9D162fZBy8Dbd2zqDIjBE=/443x0/filters:no_upscale():max_bytes(150000):strip_icc()/population-56a8fa835f9b58b7d0f6e913.GIF)

*A time series graph of the population of the United States from 1900 to 2000. — C.K. Taylor*

## Techniques and Tools for Analyzing Time Series Data

To analyze a year's daily closing stock prices, list the daily [closing prices](https://www.investopedia.com/terms/c/closingprice.asp) in order. This creates a year-long time series.

You can use technical analysis tools to check if a stock’s time series shows seasonality. This helps identify if the stock has regular peaks and troughs each year. Analysis in this area would require taking the observed prices and correlating them to a chosen season. This can involve traditional seasons like summer and winter or retail periods like holidays.

Alternatively, record how a stock's price changes relate to economic factors like the [unemployment rate](https://www.investopedia.com/terms/u/unemploymentrate.asp). Correlating data points with an economic variable shows patterns where they depend on each other.

> **Important**
>
> A challenge with time series data is autocorrelation, where each variable depends on its previous value, potentially biasing results.

## Predictive Methods in Time Series Forecasting

Time series forecasting uses past values and patterns to predict future trends. It often involves trend analysis, cyclical fluctuations, and seasonality. Success in forecasting is never guaranteed.

The [Box-Jenkins Model](https://www.investopedia.com/terms/b/box-jenkins-model.asp), for instance, is a technique designed to forecast data ranges based on inputs from a specified time series. It forecasts data using three principles: [autoregression](https://www.investopedia.com/terms/a/autoregressive.asp), differencing, and [moving averages](https://www.investopedia.com/terms/m/movingaverage.asp). These three principles are known as p, d, and q, respectively. Each principle is used in the Box-Jenkins analysis, and together they are collectively shown as an [autoregressive integrated moving average, or ARIMA](https://www.investopedia.com/terms/a/autoregressive-integrated-moving-average-arima.asp) (p, d, q). ARIMA can be used, for instance, to forecast stock prices or earnings growth.

Another method, known as [rescaled range analysis](https://www.investopedia.com/terms/r/rescaled-rangeanalysis.asp), can be used to detect and evaluate the amount of persistence, randomness, or [mean reversion](https://www.investopedia.com/terms/m/meanreversion.asp) in time series data. The rescaled range can be used to extrapolate a future value or average for the data to see if a trend is stable or likely to reverse.

## Comparing Cross-Sectional and Time Series Analysis

Cross-sectional analysis is one of the two overarching comparison methods for stock analysis. Cross-sectional analysis looks at data collected at a single point in time, rather than over a period of time. The analysis begins with the establishment of research goals and the definition of the variables that an analyst wants to measure. The next step is to identify the cross section, such as a group of peers or an industry, and to set the specific point in time being assessed. The final step is to conduct analysis, based on the cross section and the variables, and come to a conclusion on the performance of a company or organization. Essentially, cross-sectional analysis shows an investor which company is best given the metrics that they care about.

Time series analysis, known as [trend analysis](https://www.investopedia.com/terms/t/trendanalysis.asp) when it applies to technical trading, focuses on a single security over time. In this case, the price is being judged in the context of its past performance. Time series analysis shows an investor whether the company is doing better or worse than before by the measures that they care about. Often these will be classics like [earnings per share (EPS)](https://www.investopedia.com/terms/e/eps.asp), [debt to equity](https://www.investopedia.com/terms/d/debtequityratio.asp), [free cash flow (FCF)](https://www.investopedia.com/terms/f/freecashflow.asp), and so on. In practice, investors will usually use a combination of time series analysis and cross-sectional analysis before making a decision—for example, looking at the EPS over time and then checking the industry benchmark EPS.

## What Are Some Examples of Time Series?

A time series can be constructed by any data that is measured over time at evenly spaced intervals. Historical stock prices, earnings, gross domestic product (GDP), or other sequences of financial or economic data can be analyzed as a time series.

## How Do You Analyze Time Series Data?

Statistical techniques can be used to analyze time series data in two key ways: to generate inferences on how one or more variables affect some variable of interest over time, or to forecast future trends. Unlike cross-sectional data, which is essentially one slice of a time series, the arrow of time allows an analyst to make more plausible causal claims.

## What Is the Distinction Between Cross-Sectional and Time Series Data?

A cross section looks at a single point in time, which is useful for comparing and analyzing the effect of different factors on one another or describing a sample. Time series involves repeated sampling of the same data over time. In practice, both forms of analysis are commonly used, and when available, they are used together.

## How Are Time Series Used in Data Mining?

[Data mining](https://www.investopedia.com/terms/d/datamining.asp) is a process that turns reams of raw data into useful information. By utilizing software to look for patterns in large batches of data, businesses can learn more about their customers to develop more effective marketing strategies, increase sales, and decrease costs. Time series, such as a historical record of corporate filings or financial statements, are particularly useful here to identify trends and patterns that may be forecasted into the future.

## The Bottom Line

A time series is a chronological sequence of data points, essential for analyzing trends and patterns in various fields, notably investing. By examining financial data like stock prices, earnings, or GDP, investors and analysts can identify influential factors over time.

Time series analysis enables the understanding and forecasting of asset price movements and economic variables, incorporating techniques like seasonality and trend analysis. Moreover, combining time series with cross-sectional data provides richer insights into financial performance. This analytical tool empowers investors by providing a deeper comprehension of market behavior and assisting in making informed decisions.