What type of graph is used when data needs to be compared over a specific period of time?

Time series data is gathered, stored, visualized  and analyzed for various purposes across various domains:

1. In data mining, pattern recognition and machine learning, time series analysis is used for clustering, classification, query by content, anomaly detection and forecasting.
2. In signal processing, control engineering and communication engineering, time series data is used for signal detection and estimation.
3. In statistics, econometrics, quantitative finance, seismology, meteorology, and geophysics the time series analysis is used for forecasting.

Time series data can be visualized in different types of charts to facilitate insight extraction, trend analysis, and anomaly detection. Time series visualization and dashboarding tools include the InfluxDB UI and Grafana.

The term 'time series patterns' describes long-term changes in the series. Whether measured as a trend, seasonal, or cyclic pattern, the correlation can be calculated in a number of ways (linear, exponential, etc.), and the direction may change at any given time.

Time series data is used in time series analysis (historical or real-time) and time series forecasting to detect and predict patterns — essentially looking at change over time. Following is a brief overview of each.

Time series analysis methods

Time series analysis is a method of analyzing a series of data points collected over a period of time. In time series analysis, data points are recorded at regular intervals over a set period of time, rather than intermittently or at random.

Time series analysis is the use of statistical methods to analyze time series data and extract meaningful statistics and characteristics about the data. TSA helps identify trends, cycles, and seasonal variances to aid in the forecasting of a future event. Factors relevant to TSA include stationarity, seasonality and autocorrelation.

Time series analysis can be useful to see how a given variable changes over time (while time itself, in time series data, is often the independent variable). Time series analysis can also be used to examine how the changes associated with the chosen data point compare to shifts in other variables over the same time period.

Learn more about time series analysis methods, including spectral analysis, wavelet analysis, autocorrelation, and cross-correlation.

Time series forecasting methods

Time series forecasting uses information regarding historical values and associated patterns to predict future activity.

Time series forecasting methods include:

• Trend analysis
• Cyclical fluctuation analysis
• Seasonal pattern analysis

As with all forecasting methods, success is not guaranteed. Machine learning is often used for this purpose. So are its classical predecessors: Error, Trend, Seasonality Forecast (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt-Winters.

To ‘see things’ ahead of time, time series modeling (a forecasting method based on time series data) involves working on time-based data (years, days, hours, minutes) to derive hidden insights that inform decision-making. Time series models are very useful models when you have serially correlated data. Most businesses work on time series data to analyze sales projections for the next year, website traffic, competitive positioning and much more.

Learn more about time series forecasting methods, including decompositional models, smoothing-based models, and models including seasonality.

Data visualization helps deliver clear messages to your audience. Data loses value without proper context and visualization. Charts break down complex data and assist with effective communication of results. Let’s discuss data visualization charts for trends

Data visualization helps deliver clear messages to your audience. Data loses value without proper context and visualization. Charts break down complex data and assist with effective communication of results. Let’s discuss data visualization charts for trends

Before going onward, it is better if you download add-in for Excel or for Google Sheets. Because there will be few visualization in this blog which you can easily create with ChartExpo without knowing any coding, scripting or any hustle.

Interpreting a chart is easier than scrolling through a spreadsheet. At the onset of your project, you must visualize data at your disposal. Doing so helps you interpret the results and places you in a good position to interpret and present the same.

Research extensively and find the best chart to show trends over time. Before you prepare a visual diagram, assess why you need it in the first place. Establish whether there was another way of presenting the data.

What message would you like to deliver to your audience? Decide on the variables, data points, and scale. After that, find a chart that would best go with the data. What are the different variables you would like to utilize?

With dozens of chart types available, choosing the most suitable can be a tough task. Read on, learn more about trends, and discover the best chart.

In this blog you will learn:

1. How Do You Find the Best Chart to Show Trends Over Time?
2. Line Chart – Your Best Chart to Show Trends Over Time
3. Effective Ways of Showing Change over Time Data
4. Use ChartExpo for Best Charts to Show Trend Over Time
5. What Has Changed Over Time?

How Do You Find the Best Chart to Show Trends Over Time?

Google Sheets and Microsoft Excel offer you numerous charts but if you want to opt for some third-party library CharExpo is a gem.

Video Tutorial:

Considering that you already have the data, the dilemma is in determining the best graph type. The solution lies in understanding your readers, the people you want to share the information in your possession with, i.e., the audience. Are they an experienced lot? What is their familiarity with data analysis?

Here are simple guidelines to help pick the best chart to show trends over time:

a)   Know your readers

For data presentation to bear fruit, you must identify your readers. Are they skilled executive marketers? If yes, then you have an easy task since they are already familiar with graphs, charts, and other ways of data presentation.

However, a general audience with little to no knowledge of data analysis requires something more straightforward. Here are the suggested charts for less experienced audiences:

• Bar chart
• Pie graph
• Column chart
• Single row stack chart
• Dual-axis grouped bar chart

The above are simple data presentation charts. However, these may not show information in depth. The following are what we recommend for readers that are experts in the field:

• Stacked area graph
• Dual-axis radar chart
• Sankey chart
• Map and bar chart
• Non sentimental chart
• Components trend chart

b)   Determine the visualization colors

More than anyone else, marketers perfectly know how important it is to use color in brand identity. People relate and recognize brands by their colors. What many may not realize is that data is also related to your brand.

Therefore, while presenting data in a visual format, you must retain your brand’s color schemes. Pie charts and bar graphs present you with opportunities to play around with color. Think of yourself as a designer who must win their audience by including plenty of colors.

By retaining your brand colors, readers recognize your identity and retain the information presented.

Line Chart – Your Best Chart to Show Trends Over Time

Line charts are the best visual presentation for emphasizing change over time. Consider two variables, one on the vertical axis and the second on the horizontal axis. For a better understanding, the variable on the vertical axis will remain constant while the one on the horizontal axis is continuous.

Line charts can demonstrate the change by depicting change through line segments that move from left to right. As the movement takes place, observe the slope which will move up or down.

The horizontal axis requires a variable whose values change progressively at regular intervals to facilitate measurement. The variable must be such that it allows you to make hourly, daily, weekly, or monthly observations as warranted by the situation. As the analyst, you decide on the interval size depending on the type of data under observation.

Turning our focus to the vertical axis, you will capture a numeric variable for each interval on the horizontal axis. In most cases, what appears on this axis is a statistical summary such as an average value or total across events depicted on the horizontal axis. You can also plot multiple lines for trend comparison to show data changes over a specified time frame.

Visualization Source: ChartExpo

So, if you are bored with using simple line chart and you have multivariable data over time then above chart is best suitable for you. As you can see two different y-axis with different scaling values. Right y-axis is showing percentage data where as left y-axis showing cost with dollar sign.

There is another visualizaiton available in ChartExpo add-in that is dual axis with line and bar chart.

Visualization Source: ChartExpo

What if you want multiple axis in your visualization? You don’t have to worry because you have ChartExpo in your spreadheets. Below visuizatoin is Multi-axis line chart which you can create to see how different vairables are trending over the period of time.  Unique colors line and axis easily give brilliant impression at first look to identify the data pattern and finding results.

Visualization Source: ChartExpo

You will find anaother beatiful visualization using line with area chart in ChartExpo library.

Visualization Source: ChartExpo

The shaded part is showing patient visited over time and lines are showing patient treated on that day.

So ChartExpo is not stick with simple line charts. It has different collection of charts which fullfill your requirement to show trends over time with unique ways. For example, there is another visualization you will find in ChartExpo that is Sentiment Trend Chart.  As you can see in below image how different values are showing in different colors and line over the bars are showing you the trend pattern.

Visualization Source: ChartExpo

Use ChartExpo for Best Charts to Show Trend Over Time

Data visualization possibilities are endless, with many chart options. We have identified a line chart as the best chart to show trends over time from our discussion. Nonetheless, you must narrow down on a specific one since there are numerous options at your disposal.

Likewise, dozens of tools exist to assist with deriving line graphs from data sets. Presenting change over time in a visual format can, at times, be a challenging task. Rather than seeking different connectors to pull data sets, you can rely on one add-on.

ChartExpo is a Google Sheets and Excel add-on that lightens your data visualization task. It comes with in-built charts enabling you to create a line chart with only a few clicks.

As mentioned earlier, line charts are best suited for showing trends since changes against time take a linear approach. Population growth, demand forecasts, and units sold are all examples of quantitative data.

These occurrences are best visualized through line charts. We can demonstrate how to use ChartExpo in Google Sheets by using a data set example like the one below:

To begin, open your ChartExpo add-on. If you have not installed it yet, you can directly install ChartExpo add-on for Google Sheets

Once it is installed you can find it under Extension menu as Charts, Graphs & Visualizations by ChartExpo. You can click on open to see the list of charts available.

It will load and appear on your screen’s right-hand corner. In this blog let’s start with a chart which uses multiple lines. Select the Multi Series Line Chart from the list of as shown in the image below:

Next, choose your dimensions and metrics and after that, click the “Create Chart” button.

Here is what your chart will look like on your computer screen:

Visualization Source: ChartExpo

What Has Changed Over Time?

If you go back to the original data set sale data of TV, Mobile and Sound System are all you can see. At the onset, you could see that some years had higher amounts than others. Visualizing the trend was difficult, almost impossible.

However, you can spot trends in the three very easily, you can see that TV sales were good till 2018 but after that, it started a decline.

Other useful pointers for creating an effective line chart are having a zero baseline, maintaining a sequential category, using color sparingly, removing clutter, and labeling your diagram.

You can create the above chart by clicking below of any add-in for your own favorite tool.

Effective Ways of Showing Change over Time Data

1. Let your baseline be zero for all charts

As a starting point, ensure that you plot your bars against a baseline that begins from zero. That way, your readers can see how bar lengths vary. Besides, someone can compare different bar lengths since they all have a common baseline. Also, your data comes off as genuine.

On the contrary, charts with a scale gap could result in a comparison misrepresentation. Why is that so? Bar length and actual values ratio will not match. As a result, someone reading the chart will arrive at incorrect conclusions.

2. Use a sequential order for category levels

Change over time is progressive, and this is something you must show in your charts. Therefore, as you consider plotting data, decide on the order your chart bars will follow. Usually, data analysts prefer to have the longest bar at the beginning with the shortest one at the end.

Nonetheless, a reader can still compare bar lengths regardless of the order. However, such a comparison takes a bit of their time as they must move their eyes back and forth several times. Sorting the bars in a sequence cuts a reader the slack.

There are exceptions where your categories follow an inherent order and changing them would distort your presentation. In such a situation, you let inherent ordering stay.

3. Use color meaningfully

Color is an excellent way of depicting change over time. Google Sheets and Microsoft Excel include tools that apply different colors to each bar by default. While default coloring is fine, you might end up distracting the user. Someone could interpret the colors differently, yet that is not what you intended.

To remove ambiguity, adopt meaningful use of color. For instance, you can highlight some columns where you want the reader to focus on a specific data aspect. Think of it as telling a story but with an emphasis on some characters or events.

4. Simplify the ordering of chart bars

Data presentation must be simple. Otherwise, readers will not understand your message. They will see it as just another statistical image. The order in which you arrange bar charts is critical. Except for natural order, e.g., time and age, have your bars appearing in ascending or descending order of data values. Avoid alphabetical and other arbitrary setups.

5. Label the bars according to the change they represent

Having so many labels on a chart makes it look cluttered and untidy. As a rule of thumb, get rid of elements that have no relevance to the message you wish to communicate. Label each bar with what it represents. Also, remove the horizontal and vertical axes plus grid lines.

You will realize that your bar chart looks appealing and does not have visual clutter. More so, the data you present becomes noticeable.

6. Spacing

Decide on a reasonable size for your chart, not too big or small. Also, maintain a standard width for each bar while also keeping a consistent space between them.

FAQs:

1. Which type of chart is best for showing trends over time in Google Sheets and Microsoft Excel?

The answer is now clear, line charts. Most data analysts prefer using a line chart as compared to other types. If you want to plot changes and trends over time, a line chart is your best option. Line charts compare data, reveal differences across categories, show trends while also revealing highs and lows.

2. What is the best way to show trends over time?

Numerous visualization methods exist, and all show changes over time, also referred to as trends. Examples of these methods are as follows:

• Line Graph
• Bubble Chart
• Area Graph
• Gantt Chart
• Histogram
• Heatmap

While these have numerous similarities, the difference is in how you connect data ranges to create lines.

3. What graph is best for trend?

Line graphs assist a data analyst in showing trends which could be a rise or drop, increase, or decrease. With these charts, you can compare facts by displaying quantities to arrive at a comparison. What is more, a reader can quickly establish relationships between the visually presented categories.

4. What chart is best for time-based data?

Line chart scores highly here since you can use it to map continuous data. Examples of instances where you use line graphs include identifying traffic spikes, mapping an increase or decrease in sales for a specific year, weather reports, and so on.

Wrap Up:

Raw data is impossible to interpret since it has no structure. By tabulating it in a spreadsheet, you can at least begin to realize some order. Nonetheless, data tabulations do not necessarily reveal trends over time. A perfect example is when you have tons of data in numerous spreadsheets.

Line graphs display change over time through data points spread across two axes. Straight lines connect these data points. When structuring a line graph, the horizontal axis (x-axis) is where you plot your independent data. Dependent data goes to the vertical (y-axis).

A line chart is, therefore, the best chart to show trends over time. It shows trends and data variables clearly. Besides, a line graph assists readers with making predictions for the future. However, for a data set comparison being useful, you must use the same scale on both axes.

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