Table of Contents
Overview of Working with Time and Dates
Working with time and dates is very common in data analysis, logging, measurement systems, and many real world applications. In MATLAB, time related information is represented with special data types and handled with a rich set of functions that make calculations and visualization easier and more reliable than using plain numbers or text.
This chapter gives a conceptual overview of what it means to work with time and dates in MATLAB, why MATLAB provides specific tools for this, and how this topic fits into the rest of the course. The detailed creation of time variables, formatting, date calculations, and plotting over time will each be handled in their own dedicated subchapters.
Why Time and Date Handling Needs Special Tools
Time and dates look simple at first glance, for example 2025-12-29 14:30:00, but they hide many complications. Months have different lengths, leap years exist, days can change with daylight saving time rules, and time zones differ around the world. If you try to store time only as text or as a single number without any conventions, operations such as differences, sorting, or grouping can easily become incorrect or very hard to implement.
MATLAB provides special time related data types and functions that deal with these complexities in a consistent way. Instead of manually worrying about how many seconds are in a month, or how to convert between local time and UTC, you write operations at the level of time and let MATLAB handle the details behind the scenes.
Core Time Related Data Types in MATLAB
When working with time in MATLAB at a basic level, you will usually meet three main concepts.
The first concept is modern calendar date and time with datetime. A datetime value represents a specific moment according to the calendar, such as 2025-12-29 14:30:00 in a particular time zone. It is used whenever you have real world timestamps such as sensor logs, financial series, or schedule data. datetime objects know about years, months, days, hours, minutes, seconds, and time zones.
The second concept is elapsed time with duration. A duration value represents a length of time, not a point in time. Examples include 2 hours and 30 minutes, or 0.5 seconds. You typically get a duration when you subtract two datetime values. With duration, you can add or subtract time intervals to shift events forward or backward in time.
The third concept is calendar lengths with calendarDuration. This type represents lengths like 2 months, or 1 year and 3 days, which depend on the calendar structure. The actual number of days in a month or a year can change, so these cannot be accurately replaced with a fixed number of seconds. calendarDuration is useful when dealing with schedules or periods like “one month from today.”
Other historical date formats such as datenum or datestr exist for compatibility, but in new code you normally use datetime and related types. Detailed creation and formatting of these types will be covered later in this section.
Typical Time Related Tasks in MATLAB
In many MATLAB workflows, time plays a central role. You might import a file where each row has a timestamp and some measurements. Once those timestamps are converted into time aware variables, you can perform operations that are easier and less error prone than if you operated on raw text.
Common tasks include calculating elapsed times between events, such as how long a machine was running or how much time passed between transactions. You can also align multiple data sets to the same timeline, for example temperature readings and power consumption readings that were recorded at different sampling rates. MATLAB functions understand datetime values in many operations, and handle sorting and joining by time in a natural way.
Another frequent task is grouping or summarizing data by periods such as days, weeks, or months. When your time information is stored as datetime, you can easily extract components like the year or month, or create new time vectors that represent regular time steps, then aggregate your data according to these periods.
Formatting and presenting time for humans is also important. You often need to display time in different formats, such as only showing the date, or using a specific pattern like dd.MM.yyyy. Although time values are stored in a standardized internal form, MATLAB lets you control how they appear when displayed or plotted.
Time and Dates in Data Analysis and Visualization
Working with time is often combined with other tools you learn in this course. For instance, when you work with tables, it is very common to include a time column that uses datetime. This supports powerful workflows where you filter, sort, or join tables using time based conditions. Time information can also serve as row labels for specialized containers, which makes it easier to select ranges like “all data from March 2025.”
When it comes to visualization, time is frequently placed on the horizontal axis of a plot. MATLAB plotting functions can handle datetime values directly. The axis then automatically formats tick labels with readable date and time strings, and adjusts them when you zoom or pan. This way, you can visualize trends over hours, days, or years without manually converting times to numeric values.
In more advanced scenarios, you may work with time series that have irregular spacing or missing timestamps. Basic handling of such data often starts simply by representing timestamps correctly, and then applying functions that recognize the time component. This chapter section will lay the foundation for these tasks by showing you how to represent dates and times, carry out basic time calculations, and plot data over time.
Integration with the Rest of the MATLAB Language
Time and date types integrate with the rest of MATLAB in a similar way to numeric arrays. You can store datetime and duration values in vectors, matrices, tables, and cell arrays. You can index them, concatenate them, and pass them to functions, provided those functions are designed to understand time related data. Many built in functions already support these types.
You can also convert between time types and other data types. For instance, you may read timestamps from a file as text, then convert them into datetime. You may also extract numeric representations, such as seconds or days, when you need to perform certain kinds of numeric calculations. Conversion details and examples are covered in the later subchapters of this section.
Throughout your programs, you should treat time variables as their own category, distinct from plain numbers or strings. This mindset helps you avoid mixing time values with unrelated quantities, and encourages you to use MATLAB features that are specifically designed for time based data.
Structure of the Time and Dates Section
The remaining subchapters in this section focus on practical aspects of working with time in MATLAB. One subchapter explains how to create and format datetime arrays and related time representations. Another subchapter covers basic time calculations, so you can compute differences, shift times, and understand how durations work. A dedicated subchapter shows how to use time values in plots, which is especially useful for visualizing sensor data, financial data, and any measurements collected over time.
As you read through these, keep in mind the central idea from this overview. Time is not just a number or a word, it is a structured piece of information that MATLAB can understand if you represent it with the right types and functions. This understanding is the basis for reliable and clear time based analysis.
Key points to remember:
Use MATLAB’s time specific types such as datetime, duration, and calendarDuration instead of plain numbers or strings for timestamps and time intervals.
Treat points in time and lengths of time as different concepts. Points in time are modeled with datetime, while elapsed time is usually modeled with duration.
Time values integrate naturally with tables, plotting, and many other parts of MATLAB, so once your data is in a proper time format, many higher level operations become simpler and less error prone.