Deriving the Benefits of Time Series in Machine Learning

Machine learning is known as the capability of a machine or system to imitate intelligent human behavior. It is a subset of artificial intelligence and is used for the completion of complexes in lesser time as well as much higher efficiency. The concept is rapidly spreading across the economy and is also making its presence felt in the education sector. This impact can specifically be seen in online education through localization, transcription, text-to-speech, and personalization. That is, machine learning helps impart personalized education based on regional influence and data gathered from transcripts as well as other sources. Considering the outline, this article explores the concept of time series in machine learning, its relevance in educational technology (Ed Tech), and how time series in ML can be used to enhance the possibilities of an educational technology program.

Time Series Forecasting

A machine learning dataset is a collection of observations and time plays an important role in machine learning datasets. In normal Machine Learning, predictions are made for new data when there may not be a possibility to determine outcomes until sometime in the future. For more clarity, machine learning analyzes data sets and a system may be able to predict actions based on trends during the actual incident. Good examples are the searches several students (probably) make when they attend Ed tech programs. These could be, “Algebraic K-theory”, “Brill–Noether theory”, and the “Morse theory”. Based on frequent searches for these items, ML enables the system to automatically provide responses when students again make similar searches. That is, when a student types “Algebraic K…” the system automatically suggests resources available.

A time series dataset is different. It derives data based on order dependence between observations: a time dimension. For example data gathered on:

• June-July (When schools normally open in an Indian academic year)
• 9 pm – 12 pm (between March and May when board exams for 10th and 12th in Indian syllabus normally occur)
• And so on.

The time dimension provides a source of additional information which is data set observations taken sequentially in time. More accurate insights can be gathered from time series machine learning.  This is specifically from time classification in data sets. These data sets could include attendance of students for online classes; their comprehension levels at different times of a day and the span of attention students give to classes, among many others.

In conclusion, the beauty of time series in ML is that a data set model can be accurately trained with certain algorithms to predict the class of a new time series. That is, based on analysis of one time series; the data for another time series can be predicted. The algorithms that can be written for time-series machine learning include:

• Distance-based (KNN with dynamic time warping)Tech.
• Interval-based (Time SeriesForest)
• Dictionary-based (BOSS, cBOSS)
• Frequency-based (RISE — like Time Series Forest but with other features)
• Shapelet-based (Shapelet Transform Classifier)

In summary, time series in machine learning has multiple advantages when incorporated in Ed Tech programs. But it must be kept in mind that there are a lot of aspects to time series.  A good understanding is required and information must be shared in a way that is comfortable to each learner. Here is the difference between "studying" and "learning". "Studying" is simply memorizing whatever the book says and "Learning" is knowing how to apply what one studies. That is what we do at SchoolforAI !

We use personalized learning strategies that could be used to give each student an individualized educational experience. Students attending our sessions are given a clear picture what machine learning as well as time series forecasting are; then they are explained the benefits; and finally comes the sessions which help them apply those benefits.

Here, the students are guided for their own learning, can follow the pace they want and make their own decisions about what to learn. Truly, SchoolforAI democratizes learning.

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