multidimensional multi-sensor time-series data analysis framework

What is Time Series Data?

Time series data is information taken at a particular duration. For instance, having a set of sensor data observed at particular equal paces, each sensor can be classified as time series. If the data is collected without any order in time, or at once, it is not time series data.

Components of Time Series Data

To analyze time series data, we need to know the different pattern types. These patterns will together create the set of observations on time series.

  • Deterministic: In this case, the effects of shocks present in the time series are eliminated.
  • Stochastic: It is the process in which the effects of shocks are never eliminated as they have permanently changed the level of the time series.
  • Climate
  • Institutions
  • Social habits and practices
  • Calendar etc.
  • Additive Model — It is the model in which the seasonal component is added with the trend component.
  • Multiplicative Model — In this model seasonal component is multiplied with the intercept if trend component is not present in the time series.

Time Series Data vs Cross-Section Data

Time Series Data is composed of collection of data of one specific variable at particular interval of time. On the other hand, Cross-Section Data is consist of collection of data on multiple variables from different sources at a particular interval of time. Collection of company’s stock market data at regular interval of year is an example of time series data. But when the collection of company’s sales revenue, sales volume is collected for the past 3 months then it is taken as an example of cross-section data. Time series data is mainly used for obtaining results over an extended period of time, but cross-section data focuses on the information received from surveys at a particular time.

What is Time Series Analysis?

Analysis is performed in order to understand the structure and functions produced by the time series.

  • In the time domain
  • In the frequency domain
  • Decomposing the time series
  • Identifying and modeling the time-based dependencies
  • Forecasting
  • Identifying and model the system variation

Need of Time Series Analysis

In order to model successfully, the time series is important in machine learning and deep learning. Time series analysis is used to understand the internal structure and functions that are used for producing the observations. Time Series analysis is used for -

  • Descriptive — Patterns are identified in correlated data. In other words, the variations in trends and seasonality in the time series are identified.
  • Explanation — Understanding and modeling of data is performed.
  • Forecasting — The prediction from previous observations are performed for short term trends.
  • Invention Analysis — Effect performed by any event in time series data, is analyzed.
  • Quality Control — When the specific size deviates, it provides an alert.

Applications of Time Series Analysis

Few Time-Series Application Area Examples

What is MDSA?

MSDA is an open source low-code Multi-Sensor Data Analysis library in Python that aims to reduce the hypothesis to insights cycle time in a time-series multi-sensor data analysis & experiments. It enables users to perform end-to-end proof-of-concept experiments quickly and efficiently. The module identifies events in the multidimensional time series by capturing the variation and trend to establish relationships aimed towards identifying the correlated features helping in feature selection from raw sensor signals.

  1. Time series analysis.
  2. The variation of each sensor column wrt time (increasing, decreasing, equal).
  3. How each column values varies wrt other column, and the maximum variation ratio between each column wrt other column.
  4. Relationship establishment with trend array to identify most appropriate sensor.
  5. User can select window length and then check average value and standard deviation across each window for each sensor column.
  6. It provides count of growth/decay value for each sensor column values above or below a threshold value.
  7. Feature Engineering


Prototype for feature/sensor selection from multi-dimensional heterogeneous/homogeneous time series multi-sensor data. The intuitive representation of the framework is as shown below.

Pictorial representation of multi-dimensional time series data feature selection

Features Include:-

Core Functionalities in MSDA

MSDA Workflow:-

MSDA algorithm workflow

Terminal Installation:-

The easiest way to install msda is using pip.

Install in Jupyter Notebook:-

Who should use MSDA?

MSDA is an open source library that anybody can use. In my view, the ideal target audience of MSDA is:

  • Students.
  • Researchers for quick poc testing.
  • Experienced Data Scientists who want to increase productivity.
  • Citizen Data Scientists who prefer a low code solution.
  • Data Science Professionals and Consultants involved in building Proof of Concept projects.


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Ajay Arunachalam

Ajay Arunachalam

Data Science Manager; AWS Certified ML Specialist; AWS Certified Cloud Solution Architect; Power BI Certified