multidimensional multi-sensor time-series data analysis framework

What is Time Series Data?

Components of Time Series Data

  • 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

What is Time Series Analysis?

  • 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

  • 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?

  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

Overview:-

Pictorial representation of multi-dimensional time series data feature selection

Features Include:-

Core Functionalities in MSDA

MSDA Workflow:-

MSDA algorithm workflow

Terminal Installation:-

Install in Jupyter Notebook:-

Who should use MSDA?

  • 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.

CONTACT

REFERENCES

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Data Science Manager; AWS Certified ML Specialist; AWS Certified Cloud Solution Architect; https://www.linkedin.com/in/ajay-arunachalam-4744581a/

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Ajay Arunachalam

Ajay Arunachalam

Data Science Manager; AWS Certified ML Specialist; AWS Certified Cloud Solution Architect; https://www.linkedin.com/in/ajay-arunachalam-4744581a/

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