What Is a Time Series?

What Is a Time Series?

Let me explain what a time series is: it's a sequence of data points that happen in successive order over some period. You can contrast this with cross-sectional data, which just captures a snapshot at one point in time.

In investing, you use a time series to track the movement of chosen data points, like a security's price, over a specified period with recordings at regular intervals. There's no strict minimum or maximum time frame—you gather the data in a way that gives you the information you need as an investor or analyst.

Key Takeaways

A time series is essentially a data set that tracks a sample over time. It lets you see what factors influence certain variables from one period to the next. You can use time series analysis to observe how an asset, security, or economic variable changes over time. Forecasting methods based on time series apply to both fundamental and technical analysis. And while cross-sectional data is the opposite, in practice, you often use both together.

Understanding Time Series

You can apply a time series to any variable that changes over time. In investing, it's common to track a security's price over time—maybe short-term like hourly prices during a business day, or long-term like monthly closes over five years.

Time series analysis shows you how an asset, security, or economic variable evolves. It also lets you compare those changes to shifts in other variables over the same period. Beyond finance, time series appear in contexts like measuring population changes over time—think of a graph showing U.S. population growth from 1900 to 2000.

Time Series Analysis

Suppose you want to analyze daily closing stock prices for a stock over a year: you'd list all those prices in chronological order, creating a one-year daily time series.

Digging deeper, you might use technical analysis tools to check for seasonality in the stock's time series, seeing if it peaks and troughs at regular times each year. You'd correlate prices to seasons, like calendar ones (summer, winter) or retail ones (holidays).

You could also relate share price changes to an economic variable like unemployment rates, observing patterns of dependency. Keep in mind, one issue with time series data is autocorrelation—each variable depends on its prior state, which can bias your results.

Time Series Forecasting

Time series forecasting uses historical values and patterns to predict future activity, focusing on trends, cyclical fluctuations, and seasonality. Like all forecasting, it's not foolproof.

Take the Box-Jenkins Model: it forecasts data using autoregression, differencing, and moving averages—known as p, d, and q. Together, they form ARIMA (p, d, q), which you can use to forecast stock prices or earnings growth.

Another approach is rescaled range analysis, which detects persistence, randomness, or mean reversion in data, helping you extrapolate future values and assess trend stability.

Cross-Sectional vs. Time Series Analysis

Cross-sectional analysis looks at data from a single point in time, starting with research goals, defining variables, identifying a group like peers or an industry, and assessing at that specific time. It helps you see which company performs best on your metrics.

Time series analysis, or trend analysis in technical trading, focuses on a single security over time, judging its past performance on metrics like EPS, debt-to-equity, or free cash flow. In practice, you combine both: check EPS over time, then compare to industry benchmarks.

What Are Some Examples of Time Series?

You can construct a time series from any data measured over time at even intervals—historical stock prices, earnings, GDP, or other financial or economic sequences.

How Do You Analyze Time Series Data?

Use statistical techniques to infer how variables affect each other over time or to forecast trends. Unlike cross-sectional data, time series allow more plausible causal claims due to the direction of time.

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

Cross-sectional data is a single point in time for comparisons or descriptions, while time series involves repeated sampling over time. You often use both together.

How Are Time Series Used in Data Mining?

In data mining, time series like corporate filings or financial statements help identify trends and patterns for forecasting, aiding businesses in marketing, sales, and cost reduction.

The Bottom Line

A time series is a sequence of numerical data points in successive order. In investing, it records data points like a security's price at regular intervals over a period. This analysis reveals influencing factors and changes in assets or variables. Various financial data, from stock prices to GDP, can be analyzed this way.