Handling Multi-Variate Time Series Data

When it comes to time series analysis, it is not uncommon to encounter data that is not just dependent on a single variable but on multiple variables. This multi-variate time series data poses unique challenges and requires special handling techniques. In this article, we will explore some methods to handle and analyze multi-variate time series data using Python.

Understanding Multi-Variate Time Series Data

In a multi-variate time series, each observation consists of multiple variables recorded at different points in time. For example, consider a dataset where we track the daily temperature, humidity, and air pressure readings for a particular location. In this case, we have three variables (temperature, humidity, and air pressure) recorded over time.

In addition to the variation observed in each variable independently, there may also be inter-dependencies or relationships between the variables. These relationships can be analyzed to gain deeper insights into the data.

Preprocessing Multi-Variate Time Series Data

Before diving into the analysis, it is essential to preprocess the multi-variate time series data. Here are some common preprocessing steps:

  1. Handling missing values: Multi-variate time series data often has missing values. These missing values can be filled using various techniques such as interpolation, forward-fill, or backward-fill, depending on the context of the data.

  2. Normalization: It is crucial to normalize the variables to a common scale. Variables with different ranges can skew the analysis, so scaling them to a similar range (e.g., using Min-Max scaling) helps in fair comparison.

  3. Feature engineering: Sometimes, it is beneficial to create new features from the existing variables to capture additional information or relationships. For instance, deriving daily averages or weekly moving averages from daily temperature readings.

Visualizing Multi-Variate Time Series Data

Visualizing multi-variate time series data can provide useful insights. Here are a few visualization techniques:

  1. Line Plots: Line plots can be used to visualize each variable separately over time. This helps to observe the trends and patterns in individual variables.

  2. Heatmaps or Correlation Plots: Heatmaps or correlation plots provide a visual representation of the relationships between variables. Correlation coefficients can be calculated to quantify the strength and direction of these relationships.

  3. Multi-Dimensional Time Series Plots: Multi-dimensional time series plots can be used to plot multiple variables simultaneously, either in separate subplots or overlaid on the same plot. This allows us to visualize the relationships and dependencies between variables more directly.

Analyzing Multi-Variate Time Series Data

Once the data is preprocessed and visualized, various analytical techniques can be applied to gain insights from the multi-variate time series data. Some methods include:

  1. Auto-Regressive Integrated Moving Average (ARIMA): ARIMA models can be extended to handle multi-variate time series data by including lagged values of multiple variables in the model. This captures the inter-dependencies between the variables.

  2. Vector Autoregression (VAR): VAR models are specifically designed for multi-variate time series data, where each variable is modeled as a linear combination of its past values and the past values of other variables. VAR models can help forecast the values of multiple variables simultaneously.

  3. Granger Causality Analysis: Granger causality analysis is used to measure the causal relationships between variables in a time series. It can help determine whether one variable can be used to predict another variable, providing insights into the underlying dependencies.


Handling multi-variate time series data requires special techniques to explore the relationships between variables and gain meaningful insights. Python provides various libraries and methods to preprocess, visualize, and analyze multi-variate time series data. By applying these techniques, we can uncover hidden patterns, forecast future values, and make data-driven decisions.

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