Classifying Time Series using Feature Extraction

August 25, 2022

An introduction to the time series classification

The time series classification has become one of the most important research domains in the recent few years due to its massive number of practical applications in multiple sectors. A lot of industries including hospitals, transportation, business, and hotels are making use of the time series classification.  

Some of the practical examples of time series classification in machine learning are the detection of climate temperature, encountering abnormality in the stock market, and discerning the heartbeat pattern of patients in hospitals. Furthermore, a lot of industries have a huge curiosity in this domain because time series classification can help in efficient resource allocation and improve the business revenue by a great margin.

Before getting to know more about the time series classification in machine learning we need to get acquainted with the common terms which are extensively used to understand the time series classification. Some of these terms are time series analysis, time series classification, and time series data sets. Let us understand them comprehensively from below.

Time series analysis

Time series analysis is a special way of evaluating a series of data points that have been accumulated over some time. What makes time series analysis different from any other evaluation method is the fact that time series analysis makes use of different variables and showcases that the variables can transform over time. 

Moreover, the collection of data points for time series analysis is not random. The time series analysis models are constructed with the help of multiple machine learning technologies.

Time series classification

Time series classification categorizes different data points that have been collected over an interval of time depending on their action or conduct. Through time series classification you can find data sets that are behaving abnormally when you are correlating them with other data sets.

Time series data sets

The time series data sets are an amalgamation of observation that has been gathered after repeated measurement over a specified period. This data set series is heavily influenced by the order of points which means if you change the order of data points the entire data set meaning will transform.

Time series classification with feature extraction

Detecting abnormal and inconsistent time series is steadily turning into a common practice for multiple organizations and industries. These unusualness in data series are helping the companies and sometimes the industries to anticipate the future market and take robust business decisions. In this scenario, you can utilize the feature extraction methodology for time series classification.

Feature extraction helps in pulling out information from a time series and illustrates the entire data set as a feature vector. All of these features can be obtained from the methodical time series analysis. 

Some of the time series features include distribution, stationarity, correlation structure, scaling properties, and distribution. The above-mentioned features can help the time series to squeeze into a variety of time series models. Not to mention, most of the time series features that narrate the time series information are statistical.

Challenge of collecting a huge amount of data 

Everyday time series data is collected in huge amounts from numerous data sources and different domains. Especially social media platforms are a heterogeneous data source from which a humongous amount of data is generated in a fraction of a second. For time series classification the colossal amount of data and its dynamic nature is constantly creating new challenges.

The complexity of big data cannot be tackled through normal means or by using traditional classification methods, for example, instance-based classification. The traditional methods won’t be able to precisely identify the abnormalities in time series. 

Nevertheless, feature-based time series classification is flexible and more expressible for data noise and missing data. It can easily help in recognizing the concealed patterns and systematically preprocessing the data.

Reference links:

https://medium.datadriveninvestor.com/time-series-classification-using-feature-extraction-16209570a22e

https://developer.ibm.com/learningpaths/get-started-time-series-classification-api/what-is-time-series-classification/

https://paperswithcode.com/task/time-series-classification

https://www.analyticsvidhya.com/blog/2019/01/introduction-time-series-classification/

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Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

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Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

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https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

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Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/ 

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html

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