LeetCode 346: QUEUE SLIDING WINDOW PATTERN : Calculate Running Averages in Real-Time Data Streams

Introduction:
Welcome to another exciting tutorial on solving LeetCode problems! In this video, we will tackle LeetCode Problem 346: Moving Average from Data Stream. This problem challenges us to efficiently compute the moving average of a stream of data in real time. If you're looking to improve your algorithmic thinking and enhance your problem-solving skills, this is the perfect problem for you. Let's dive in and understand how we can solve it step-by-step.
Problem Statement:
The problem statement for LeetCode 346 is straightforward. We are required to design a class that calculates the moving average of all integers in the sliding window of a given size. Specifically, the class should support the following operations:
Initialization with a window size.
Adding a new integer from the data stream.
Calculating the moving average of the integers in the sliding window.
Understanding the Moving Average:
A moving average is a statistical measure used to analyze data points by creating a series of averages of different subsets of the full data set. In the context of this problem, we are focusing on a simple moving average (SMA), where the average is calculated over a fixed number of most recent elements from the data stream.
Examples and Explanations:
Let's consider an example to better understand the problem. Suppose our window size is 3, and we receive a data stream of integers: [1, 10, 3, 5]. Our task is to compute the moving average after each new integer is added to the window.
Adding 1 to the window, the moving average is 1.0 since there's only one element.
Adding 10 to the window, the moving average is (1+10)/2 = 5.5.
Adding 3 to the window, the moving average is (1+10+3)/3 = 4.67.
Adding 5 to the window, the window now contains [10, 3, 5], and the moving average is (10+3+5)/3 = 6.0.
This example illustrates how the moving average changes as new elements are added to the window.
Approach and Solution:
To solve this problem efficiently, we need to manage the elements within the sliding window and ensure that the computation of the moving average is quick and scalable. A naive approach could involve summing all elements within the window every time a new element is added, but this would be inefficient for larger window sizes. Instead, we will use a queue data structure to maintain the elements within the window and a variable to keep track of the sum of these elements.
Step-by-Step Solution:
Initialization:
Initialize a queue to store the elements within the window.
Initialize a variable to keep track of the sum of the elements within the window.
Set the maximum size of the window.
Adding a New Integer:
When a new integer is added to the stream, add it to the queue.
Add the value of the new integer to the sum variable.
If the size of the queue exceeds the maximum window size, remove the oldest element from the queue and subtract its value from the sum variable.
Calculating the Moving Average:
The moving average is calculated by dividing the sum variable by the number of elements currently in the queue.
This approach ensures that each operation (adding a new integer and calculating the moving average) is performed in constant time, O(1), making it highly efficient for real-time data streams.
Use Cases and Applications:
The concept of moving averages is widely used in various fields such as finance, engineering, and data analysis. Here are some practical applications:
Financial Markets:
Moving averages are used to analyze stock prices, track trends, and identify potential buy or sell signals.
Signal Processing:
In engineering, moving averages are used to smooth out short-term fluctuations in signals and highlight long-term trends.
Data Analysis:
Moving averages help in analyzing time-series data by reducing noise and providing a clearer picture of underlying trends.
Understanding how to efficiently compute moving averages is a valuable skill for anyone working with real-time data streams or time-series data.
Conclusion:
In this video, we explored how to solve LeetCode Problem 346: Moving Average from Data Stream. We discussed the problem statement, understood the concept of moving averages, and walked through a step-by-step solution using a queue to efficiently manage the sliding window. This approach ensures that each operation is performed in constant time, making it suitable for real-time data streams.
We also touched upon various practical applications of moving averages in different fields, highlighting the importance of this concept. By mastering this problem, you have gained valuable skills that are applicable in many real-world scenarios.
If you found this video helpful, please like, subscribe, and share it with others who might benefit. Feel free to leave any questions or comments below, and I'll be happy to help. Stay tuned for more tutorials on solving LeetCode problems and enhancing your problem-solving skills. Thank you for watching!

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