Last updated on 2024年4月9日

What are the best statistical graphics for identifying seasonality in time series data?

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Seasonality is a common feature of many time series data, especially in fields like economics, finance, ecology, and meteorology. It refers to the periodic fluctuations in the data that occur at regular intervals, such as months, quarters, or years. Identifying and analyzing seasonality can help you understand the patterns, trends, and cycles in your data, and improve your forecasting and decision making. But how do you visualize seasonality in your time series data? What are the best statistical graphics for this purpose? In this article, we will explore some of the most effective and widely used graphical methods for detecting and displaying seasonality in time series data.

本文章的要点总结

Overlay a moving average:

Adding a moving average to a line plot smooths out short-term noise, making the seasonal patterns more pronounced and easier to identify.
Seasonal decomposition:

Utilizing Seasonal-Trend decomposition using Loess (STL) breaks down time series data into trend, seasonality, and remainder for clear analysis of each component.

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1 Line plots

One of the simplest and most common ways to plot time series data is to use a line plot, where the x-axis represents the time and the y-axis represents the variable of interest. A line plot can show the overall behavior of the data, such as the direction, magnitude, and variability of the changes over time. It can also reveal the presence of seasonality, if the data shows regular peaks and troughs that correspond to specific time periods. For example, a line plot of monthly sales data might show higher values in December and lower values in January, indicating a seasonal effect. However, a line plot can also be misleading or confusing, if the data has multiple sources of variation, such as trend, noise, or outliers. In that case, a line plot might not clearly show the seasonal pattern, or it might mix up different seasonal cycles. For example, a line plot of daily temperature data might not distinguish between the annual and the daily cycles, or it might be distorted by extreme values.

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Ashik Radhakrishnan M

?? Chartered Accountant | Quantitative Finance Enthusiast | Data Science & AI in Finance | Proficient in Financial Accounting, Auditing and Taxation.
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Line plots serve as powerful tools in identifying seasonality within time series data. Constructing a time series line plot, where data points are connected by lines, enables the visual detection of recurring patterns indicative of seasonality. Enhancing the interpretability of these patterns can be achieved by overlaying a moving average on the line plot, which smooths out short-term fluctuations and highlights underlying seasonal trends. Moreover, the Seasonal-Trend decomposition using Loess (STL) technique further refines the analysis by breaking down the line plot into components like trend, seasonality, and residuals, offering a comprehensive understanding of the temporal patterns.

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Manos Papadakis

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Time series showing periodicity are the best. time on the x axis everything else on the y axis. You can often use two y axes but be careful with the unit length on the y axis. Don't create visually misleading graphs.

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Vineet Yadav

Machine Learning & Artificial Intelligence||MLOps & Cloud computing||Generative AI & LLM Models ||Computer Vision & NLP||Semantic Web & Knowledge Graph||Graph NN & Graph ML||8x Azure||3X GCP|| IIIT Hyderabad
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Generally, time series consists of two variables. The first variable represents date or time and the second variable is dependent variable or outcome. For example, in demand forecasting, we want to predict product demand. Here product demand is outcome or dependent variable. Lineplot is used to project the journey of the dependent variable with time. In other words, it shows variations of outcome y with time. In this journey, time is generally represented by X-axis and dependent variable is represented by the Y axis. Time series is not a linear path. The journey witnesses more zigzag curves and oscillations, as time series contains seasonality and cyclical component. The direction of journey is defined by trend component of time series.

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2 Seasonal subseries plots

A seasonal subseries plot is a graphical technique that can help you isolate and compare the seasonal effects in your time series data. It works by dividing the data into groups based on the seasonal period, such as months or quarters, and then plotting each group as a separate line on the same graph. The x-axis represents the time within each group, and the y-axis represents the variable of interest. A seasonal subseries plot can show you how the data varies within each season, and how consistent or variable the seasonal pattern is across different seasons. For example, a seasonal subseries plot of monthly sales data might show you how the sales change from January to December within each year, and how similar or different the sales are in the same month across different years.

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Ankit Malik

Distinguished AI Scientist | Data Engineer | AI Agents
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One tool that has become my go-to for unraveling seasonal patterns is the Seasonal Subseries Plot. Imagine being able to dissect your time series data into distinct groups based on the seasons – that's the essence of a Seasonal Subseries Plot. Whether it's months, quarters, or any other seasonal period, this method creates subsets that show the underlying patterns. Visualizing each season as a separate line on the same graph is where the magic happens. The x-axis captures the chronological progression within each season, while the y-axis showcases the variable of interest. The plot lays bare the variations within each season.

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3 Seasonal decomposition plots

A seasonal decomposition plot is a graphical technique that can help you decompose your time series data into three components: trend, seasonality, and remainder. It works by applying a statistical method, such as moving average or STL, to estimate and remove the trend and the seasonal effects from the data, and then plotting each component separately on the same graph. The x-axis represents the time, and the y-axis represents the variable of interest. A seasonal decomposition plot can show you how each component contributes to the overall behavior of the data, and how they interact with each other. For example, a seasonal decomposition plot of monthly sales data might show you how the sales grow or decline over time (trend), how the sales fluctuate around the trend (seasonality), and how the sales deviate from the trend and the seasonality (remainder).

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4 Box plots

A box plot is a graphical technique that can help you summarize and compare the distribution of your time series data across different groups. It works by dividing the data into groups based on the seasonal period, such as months or quarters, and then plotting a box for each group on the same graph. The box shows the median, the interquartile range, and the outliers of the data within each group. The x-axis represents the groups, and the y-axis represents the variable of interest. A box plot can show you how the data varies across different seasons, and how similar or different the data is within each season. For example, a box plot of monthly sales data might show you how the median, the spread, and the outliers of the sales differ from month to month.

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Ashik Radhakrishnan M

?? Chartered Accountant | Quantitative Finance Enthusiast | Data Science & AI in Finance | Proficient in Financial Accounting, Auditing and Taxation.
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Box plots are effective tools for uncovering seasonality in time series data. These plots display the distribution of data, emphasizing quartiles and potential outliers. Box plots help identify variations in data at different time points. Seasonal patterns often manifest as changes in the spread or median of the box. Outliers can also indicate irregularities in the time series. Utilizing box plots in time series analysis, especially when examining factors like monthly sales data, provides a concise visual representation of seasonality, aiding in the interpretation and strategic use of temporal patterns for data-driven decision-making.

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Manish Rawat, MSc.

Research Assistant @ UH | Data Scientist | Ex-Flex
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Boxplots detect time series data seasonality by visualizing central tendency and spread (median and interquartile range). Seasonal variations from the median are highlighted. Monitoring changes over time with boxplots for different intervals reveals dynamic seasonality assessments, with trends in central tendency or spread changes indicating shifts. Statistical tests like autocorrelation and spectral analysis complement boxplots, offering quantitative seasonality measures and validating observations.

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5 Polar plots

A polar plot is a graphical technique that can help you visualize the cyclical nature of your time series data. It works by transforming the data into polar coordinates, where the angle represents the time and the radius represents the variable of interest. The data is then plotted as a line or a scatter plot on a circular graph. A polar plot can show you how the data changes over one or more cycles, and how symmetric or asymmetric the data is around the circle. For example, a polar plot of daily temperature data might show you how the temperature varies over a year, and how similar or different the temperature is in different seasons.

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Ashik Radhakrishnan M

?? Chartered Accountant | Quantitative Finance Enthusiast | Data Science & AI in Finance | Proficient in Financial Accounting, Auditing and Taxation.
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Polar plots offer a unique perspective on seasonality in time series data. These plots use radial and angular coordinates, making them ideal for visualizing cyclical patterns. A polar plot can reveal periodic fluctuations around the center, showcasing the recurrence of patterns at specific angles. This representation is particularly useful for capturing circular or cyclic trends in time series data.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Carlos Verano

Gobierno y Transformación Digital | MBA | PMP? | SMPC? | ITIL4? | Auditor ISO/IEC 27001 | CIP
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En mi opinión, lo más importante a tener en cuenta al identificar la estacionalidad en series temporales es utilizar una combinación de estos gráficos y técnicas. Cada uno aporta una perspectiva diferente y complementaria para entender la estructura estacional de los datos. Es recomendable acompa?ar el análisis visual con pruebas estadísticas formales, como el test de Friedman o el test de Kruskal-Wallis, para evaluar la significancia de los patrones estacionales observados. Estos juntos constituyen un enfoque sólido y efectivo para identificar y caracterizar la estacionalidad en series temporales.

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What are the best statistical graphics for identifying seasonality in time series data?

1

2

3

4

5

6

1 Line plots

2 Seasonal subseries plots

3 Seasonal decomposition plots

4 Box plots

5 Polar plots

6 Here’s what else to consider

Statistics

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What are the best statistical graphics for identifying seasonality in time series data?

1

2

3

4

5

6

1 Line plots

2 Seasonal subseries plots

3 Seasonal decomposition plots

4 Box plots

5 Polar plots

6 Here’s what else to consider

Statistics

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感谢您的反馈

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