April 13th, 2026
What Is Time Series Analysis?: Complete Guide + How to Use It
By Tyler Shibata · 17 min read
What is time series analysis?
Time series analysis is a method you use to study data collected over time to identify patterns, trends, and changes. What sets it apart from other types of analysis is that the order of your data points matters. For example, a single January figure on its own tells you very little, but comparing it to the same month across multiple years reveals the real pattern.
The more consistent and complete your data is across a set interval like daily, weekly, or monthly, the more reliable your analysis becomes.
Why do businesses use time series analysis?
Time series analysis helps you answer the questions that drive decisions, like why sales dropped last quarter or what to expect next month.
Here's where it adds the most value:
Forecasting: Use historical patterns to predict future outcomes like next quarter's revenue or expected product demand.
Tracking performance over time: See whether your key metrics trend up, down, or stay steady instead of checking a single number in isolation.
Detecting anomalies: Spot unusual spikes or drops in your data early before they turn into bigger problems.
Planning and budgeting: Use past seasonal patterns to make better decisions about staffing, inventory, and spending.
Campaign and marketing analysis: Measure how a campaign affects traffic, conversions, or revenue over time, not just at one point.
Key components of time series data
Most time series datasets include four core elements. Learning to spot each one helps you understand what your data is telling you.
Here's how each one works:
Trend: The long-term direction of your data, whether it's climbing, falling, or staying flat. A SaaS company tracking monthly recurring revenue over three years would see a trend line showing overall growth, even if individual months vary.
Seasonality: Patterns that repeat at regular intervals, like a spike in retail sales every November or a dip in the B2B pipeline every August when buyers go on vacation. Seasonality becomes predictable once you have enough historical data, and in my experience, it's often the first pattern marketers notice.
Cycles: Longer-term fluctuations that don't follow a fixed schedule. An economic slowdown that affects your sales over 18 months is a cycle, not a seasonal pattern. These are harder to predict because their timing and length vary.
Noise: The random variation left over after you account for trend, seasonality, and cycles. A one-off spike from a viral social post or a data entry error both show up as noise. Separating noise from meaningful signals is one of the more difficult parts of time series analysis, and I've found it's where a lot of manual analysis goes wrong.
Types of time series data
Time series, cross-sectional, and pooled data
These three categories describe how your data was collected. Here's how they differ:
Time series data: Observations recorded at consistent intervals over time, like monthly revenue or daily website visits.
Cross-sectional data: A snapshot of multiple subjects at a single point in time, like surveying 500 customers. This type of data has no time progression.
Pooled data: A dataset that merges observations from different time periods into one. For example, combining quarterly sales records from the past 2 years into a single dataset gives you more to work with, even if the underlying records aren't tracking the same customers or transactions each quarter.
Univariate vs. multivariate
Stationary vs. non-stationary
Common time series analysis techniques
There are several methods analysts use to make sense of time series data, and the right one depends on what you're trying to find. I'll keep these practical rather than mathematical, because for most business users, knowing when to use a technique matters more than understanding how it works under the hood.
Here are the most common techniques:
Moving average: Smooth out short-term fluctuations by averaging data points across a rolling time window. If your weekly sales data is noisy and hard to read, a moving average helps you see the underlying trend more clearly. It's one of the simplest techniques and often one of the most useful for a quick read on direction.
Exponential smoothing: Similar to a moving average, but it gives more weight to recent data points than older ones. This makes it a better fit for forecasting when recent trends matter more than historical ones, like predicting next month's demand based on the last few months of sales.
ARIMA (Autoregressive Integrated Moving Average): A forecasting model that uses the history of your data, including past values and past errors, to predict what comes next. It's a good fit when your numbers follow a recognizable pattern over time rather than moving randomly. Common uses include revenue forecasting and inventory planning.
Decomposition: Split your time series into its core components of trend, seasonality, and noise. It doesn't forecast, but it's one of the clearest ways to understand what's driving your numbers. I find it particularly useful early in an analysis when you're trying to understand what you're looking at.
Real-world examples of time series analysis
An example of a monthly revenue time series
Time series analysis shows up across almost every business function. Here are some of the most common use cases:
Stock price trends: Tracking how a company's share price moves over time is a classic time series problem. You're looking for patterns, momentum shifts, and how external events affect prices.
Company revenue and financial performance: Analyzing a company's revenue, profit margins, or earnings over multiple quarters helps you spot growth trends, seasonal dips, and inflection points. This is a common use case in finance and private equity, where you evaluate a company's trajectory before making a decision.
Marketing campaign performance: Tracking conversions, traffic, or ad spend over a campaign window shows you whether results are trending in the right direction and how performance compares to previous periods.
Customer churn and retention: Plotting churn rate month over month helps you see whether attrition is seasonal, tied to a product change, or part of a longer trend. I've seen this catch problems that a single monthly report would have buried.
Inventory and demand planning: Retailers and operations teams use time series analysis to forecast demand across seasons and avoid both overstock and stockouts.
Limitations and challenges of time series analysis
Time series analysis helps you understand your data over time, but it comes with limitations worth knowing before you dive in. Here's where it can get tricky:
Missing data: Gaps in your dataset, whether from a system outage, a reporting error, or inconsistent collection, can skew your results. Most models need a complete, consistent record to produce reliable output, and filling those gaps requires careful judgment rather than quick fixes.
Non-stationarity: Most business data shifts over time in ways that make it harder to model. A metric that trends upward or spikes every December needs to be stabilized before many analysis techniques work properly. That extra step is easy to skip if you're not aware of it.
Overfitting: A model that's too closely fitted to your historical data can fail to hold up against new data. I've seen this happen when analysts build highly specific models around a particular period, only to find the predictions fall apart when conditions change even slightly.
Seasonal drift: Seasonal patterns don't always stay consistent from year to year. Consumer behavior shifts, markets evolve, and what looked like a reliable annual pattern can change shape over time, making older models less useful than they appear.
Data volume requirements: Time series analysis generally needs a substantial amount of historical data to produce meaningful results. If you're working with a newer product or a recently launched metric, you may not have enough history to draw reliable conclusions yet.
How to run a time series analysis with Julius
There are several tools that can handle time series analysis, but many require some technical setup. For this walkthrough, we'll use Julius, which lets you explore data by typing questions in natural language.
Here’s how:
Step 1: Source or upload your data
Start by bringing your data into Julius. You can upload a CSV or Excel file, connect a data source like Postgres, Snowflake, or BigQuery, or skip the upload entirely if you're working with public or financial data. Julius can search the web for relevant public datasets or pull structured financial data for over 17,000 companies through its Financial Datasets integration, so there's often no file prep required to get started.
Step 2: Ask your question in plain English
Type your question into the chat interface and send your first message. Something like "show me monthly revenue trends over the last two years" or "plot customer churn rate by quarter and identify any seasonal patterns" is enough to get started. Julius will interpret your question, write the code, and run the analysis for you.
Step 3: Review your output and ask follow-up questions to refine your results
Julius returns a chart along with an explanation of what the data shows. From there, you can ask follow-up questions, break the data down by a different variable, or ask Julius to separate the trend from the seasonal component.
Step 4: Export or share your results
Want to run time series analysis without the technical setup? Try Julius
Understanding what a time series analysis is gets you halfway there. Julius helps with the rest. You can connect your data, ask questions in plain English, and get charts and reports back without writing code.
Here’s how:
Data search: Type your question, and Julius can search for relevant public data or pull live financial market data for over 17,000 companies through its Financial Datasets integration, so you can start your analysis before you have a dataset ready.
Direct connections: Link databases like PostgreSQL, Snowflake, and BigQuery, or integrate with Google Ads and other business tools. You can also upload CSV or Excel files. Your analysis can reflect live data, so you’re less likely to rely on outdated spreadsheets.
Repeatable Notebooks: Save an analysis as a notebook and run it again with fresh data whenever you need. You can also schedule notebooks to send updated results to email or Slack.
Smarter over time: Julius includes a Learning Sub Agent, an AI that adapts to your database structure over time. It learns table relationships and column meanings as you work with your data, which can help improve result accuracy.
Quick single-metric checks: Ask for an average, spread, or distribution, and Julius shows you the numbers with an easy-to-read chart.
Built-in visualization: Get histograms, box plots, and bar charts on the spot instead of jumping into another tool to build them.
One-click sharing: Turn an analysis into a PDF report you can share without extra formatting.
Ready to start spotting trends and patterns in your historical data? Try Julius for free today.
Frequently asked questions
What is the difference between time series analysis and forecasting?
Time series analysis looks at historical data to find patterns, while forecasting uses those patterns to predict future values. Analysis comes first and tells you what has been happening and why. Forecasting builds on that foundation to estimate what comes next. You need the analysis before the forecast can be reliable.
What is the most effective way to visualize time series data?
A line chart is the most effective way to visualize time series data because it shows how a variable moves over time at a glance. For more detailed analysis, decomposition plots are useful because they break your data into its individual components so you can see what's driving the overall pattern.
What types of data are not suitable for time series analysis?
Data without a time component is not suitable for time series analysis. A one-time customer survey or a single month of sales figures won't work because there's no sequence to analyze. The same goes for data that's too erratic or random, since time series analysis works best when there's some kind of pattern to find.
