Skip to content

Components of Time Series Data

Introduction to Concept Drift

Last Updated : 02 Sep, 2020

If we place ourselves in a frame that differs slightly from what we usually see. For instance: when we do batch learning, i.e. learning on a fixed set of data that generates a given model, algorithms can quickly become ineffective or even counterproductive. This problem could occur because of the modification of data or the occurrence of new data constantly. This problem is known as concept drift.

A formal definition:
Concept drift is the event where the statistical properties of the class variable of the data — in other words, the target we want to predict — change over time. When a model is trained, it knows a function that maps the independent variables, or predictors, to the target variables. In other words, predicting the target variable with the help of other independent variables. In a static and perfect environment where none of these predictors nor the target changes or evolves, the model should perform as it did on day one because there’s no change. But if the predictors are changed with time, the model might change the performance, as it was trained with old data, and predicting from new data might be tough for the model because of the evolution of the predictors.
An example of such a situation is Dynamic Data (For Instance: Streaming Data), where not only do the statistical properties of the target variable change but so does its meaning. When this change happens, the mapping found by the function is no longer suitable for the new environment.

In Machine Learning and Predictive analytics, the concept drift means the statistical properties of the target variable of the data, of which the model is trying to predict, changes over time in very unpredicted ways. This leads to problems because as time passes, the predictions become less accurate. Hence of little or no use.

Let’s illustrate an example of a sensor positioned on a volcano in order to collect the temperature of the latter over time. Suppose that we collect data over several days during which it only rained. Learning about these data would allow us to obtain the following model (figure below): beyond a certain threshold, we consider that the volcano is active and if not, it is at rest.

Figure 1: Data during Rain

However, a few days later, a heatwave arrives and the temperature distribution is found changed as below (Figure 2). We can easily see that the model established earlier is no longer valid, you have to adapt it.

Figure 2: Data After Rain

We can also see the concept of concept-drift in shopping during Diwali in India. During normal days shopping goes very normally but suddenly during the time of Diwali, the shopping hikes very sudden. Below are the few statistics that are taken from here.

Figure 2: Data After Rain

Components of Time Series Data

ayushsaxena77

Improve

Article Tags :

Practice Tags :

Similar Reads

K means Clustering - Introduction

K-Means Clustering is an Unsupervised Machine Learning algorithm which groups the unlabeled dataset into different clusters. The article aims to explore the fundamentals and working of k means clustering along with its implementation. Understanding K-means ClusteringK-means clustering is a technique

Data Science 101: An Easy Introduction

Welcome to "Data Science 101: An Easy Introduction," your starting point for understanding the exciting field of data science. In today's world, turning lots of raw data into useful insights is incredibly valuable. Whether you're a student, working professional, or just curious, this guide will help

Inductive Reasoning in AI

Inductive reasoning, a fundamental aspect of human logic and reasoning, plays a pivotal role in the realm of artificial intelligence (AI). This cognitive process involves making generalizations from specific observations, which AI systems mimic to improve decision-making and predict outcomes. This a

Components of Time Series Data

Time series data, which consists of observations recorded over time at regular intervals, can be analyzed by breaking it down into four primary components. These components help identify patterns, trends, and irregularities in the data. It's often shown as a line graph to easily see patterns over ti

Conceptual Dependency (CD) Theory in Artificial Intelligence

Conceptual Dependency (CD) theory in Artificial Intelligence (AI), developed by Roger Schank in 1969, aims to enable machines to understand human language. It focuses on representing the meaning of sentences in a way that transcends specific words or languages, allowing AI systems to grasp the core

Data Transformation in Data Mining

Data transformation in data mining refers to the process of converting raw data into a format that is suitable for analysis and modeling. It also ensures that data is free of errors and inconsistencies. The goal of data transformation is to prepare the data for data mining so that it can be used to

Data Scientist Roadmap - A Complete Guide [2025]

Welcome to your comprehensive Data Science Roadmap! If youâ€™ve ever wondered, about â€œSteps or Path to Become a Data Scientistâ€, youâ€™re in the right place. This guide is perfect for Data Science for Beginners and seasoned professionals alike, covering everything from mastering Python for Data Science

What is Gradient descent?

Gradient Descent is a fundamental algorithm in machine learning and optimization. It is used for tasks like training neural networks, fitting regression lines, and minimizing cost functions in models. In this article we will understand what gradient descent is, how it works , mathematics behind it a

Agent-Environment Interface in AI

The agent-environment interface is a fundamental concept of reinforcement learning. It encapsulates the continuous interaction between an autonomous agent and its surrounding environment that forms the basis of how the agents learn from and adapt to their experiences to achieve specific goals. This

Bass Diffusion Model

The "Bass Diffusion Model," or BASS Model for short, is a mathematical model that is used to examine and forecast how new ideas and goods will be adopted and spread within a market or community. The foundation of Frank Bass's 1969 model is the idea that consumers can be divided into two groups: inno