In today’s world, the huge amounts of data that is getting generated from a multitude of sources encompass enormous hidden information. This data, when analyzed could formulate outstanding conclusions, and predict future events that would eventually help the business. The way the data is being analyzed has resulted in the emergence of the term called Machine Learning which is nothing much applying relevant tools, and techniques to build predictive models.
Python is the most used programming language for Machine Learning followed by R. While Machine Learning is a part of a much bigger concept called Data Science, one of the most popular usages of ML is in Time series classification.
In this blog, we would provide a brief intuition about time-series and would look into a use case in python.
What is Time Series Classification?
The exponential rise of the amount of the generated data has opened the door for more research area in the field of Data Science, and time series data is certainly a niche which could still be explored so much.
However, Time series classification is not a new phenomenon. It’s been around for a while and has found its usage across different domains such as hospitals, transportation, hotels, etc. Though mostly it is used in academia or research labs, with time its importance could be found in many industrial applications. The detection of an anomaly in the stock market, the identification of heartbeat patterns, and the detection of temperature in climate science are some of its practice usages.
Unlike a normal classification problem, a time series classification data has an ordered sequence of attributes. Increase in the accuracy of such classification could resolve resource issue in business, and also generate high revenues. Time series analysis, Time series classification data set, and Time series classification algorithms are some of the key terms associated with time series classification.
To represent the measurements of any quantity over a certain period of time, the time series data set is used. In the data set, the points order heavily influences the behaviour of the series, and the data set meaning could be changed with any change in the order of the points. The development of the statistical models to explain the variations in the sample data is part of the time series analysis process. The various machine learning technologies are used to develop the models.
The classification of data points based on its behaviour over a certain period of time is known as Time Series Classification. The common occurrence nowadays in organizations is to identify unusual time series. To make accurate business decisions, organizations must identify abnormal behaviour in the data. There are companies which monitor their mail servers to detect malicious time series.
One of the methods used for Time Series classification is Feature Extraction where the time series is represented as a feature vector. Some time series features examples are – entropy, co-relation structure, stationary, and so on. This set of feature vectors are used for the classification model and has resulted in better performance than instance-based classification. Some of the features introduced for time series are spikiness, crossing points, lumpiness, etc.
Traditional instance-based classification models could not accurately identify anomalous time series because of the size, and complexity of the data. A feature-based approach to Time series classification is immune to noisy data and hence makes for more accurate models. It is used for visualization purpose as well, and data could be organized automatically. In the case of Time series forecasting, it acts as a supportive mechanism.
Some Use Cases of Time Series Classification
Despite its inclination more towards research areas, Time Series classification is gradually finding its way in practical applications and helping the business grow in the process. Some of its use cases are.
- The classification of ECG signals where the electrical activity of the heart is recorded. It is used in the diagnosis of various problems of the heart and using external electrodes the signals are captured. The captured data in the electrodes represent time series data, and the different classes represent the signals which comprise of our brain’s electrical activity.
- The classification of the image is another time series classification use case which is in a time-dependent format.
- It could also be applied in classifying high-frequency sensor data used to identify object movements in their range. The change in signal strengths in multiple sensors could track an object’s direction of movement.
Example of Time Series Classification Problem
Now, we would use the Time Series Classification on Indoor User Movement Prediction problem. There would be multiple motion sensors placed across different rooms to identify an individual’s movement. The reading of the sensor could give a person’s position at an instant, and its movement could be tracked with the change in the sensor reading.
The dataset consists of the 314 MovementALL files which have the readings from sensors.
Below are the steps used to build the Time Series Classification models.
- The necessary libraries are imported
- The data is loaded into two separate data frames
- The data from the sensors is being read and stored in a list
- As the dataset was loaded in three different rooms, hence three groups are created for train, test, and validation respectively
- The data is pre-processed
- As 40 to 60 is most of the lengths of the files, hence the max or min didn’t make much sense
- The dataset is now ready and separated into train, validation, and test sets
- Building the model
- The model is trained, and the validation accuracy is calculated
- The accuracy is 0.788 which could be improved by tweaking the hyperparameters, learning rate
Time series is certainly an important concept in Data Science, and mastering it could help a business in the market. Here, we learned about time series classification, and build a predictive model on a time series data set.