Summary of 4- Lab 2:Plotting TS and Moving Averages | سلسلة تحليل السلاسل الزمنيه باستخدام تعليم الآلة عربي

Summary of Main Ideas and Concepts

The video, titled "Lab 2: Plotting TS and Moving Averages," focuses on the application of time series analysis using machine learning techniques in Arabic. It covers the process of plotting time series data, specifically stock prices, and introduces the concept of moving averages. The main ideas and lessons conveyed in the video include:

• Plotting Time Series Data:
  • Importance of visualizing time series data to identify patterns.
  • Steps to import necessary libraries and set up the plotting environment using matplotlib.
  • Reading stock price data (e.g., Apple, Microsoft, IBM) and plotting it.
• Customization of Plots:
  • Changing plot styles using different backends (e.g., matplotlib, hvplot).
  • Saving plots as images with specified resolution.
  • Creating interactive plots that allow zooming and data point inspection.
• Moving Averages:
  • Introduction to different types of moving averages: Simple Moving Average (SMA), Exponential Moving Average (EMA), and Centered Moving Average.
  • Explanation of how to calculate moving averages using rolling windows.
  • Differences in sensitivity to price changes between SMA and EMA, with EMA being more responsive to recent price movements.
• Practical Application:
  • Demonstration of how to implement moving averages on stock price data, including visual comparisons between raw data and moving averages.
  • Discussion on the implications of using different types of moving averages for short-term vs. long-term trading strategies.

Methodology and Instructions

• Import Libraries:
  • Use libraries like pandas, matplotlib, and hvplot for data manipulation and plotting.
• Data Reading and Plotting:
  • Read stock price data using pandas.
  • Use dataframe.plot() to create basic plots.
  • Customize plots with plt.style.use() to change styles.
• Saving Plots:
  • Save plots using plt.savefig() with specified filename and resolution.
• Creating Interactive Plots:
  • Use hvplot for interactive visualizations, allowing users to zoom and inspect data points.
• Calculating Moving Averages:
  • Simple Moving Average (SMA):
    • Use dataframe['column_name'].rolling(window=3).mean() for a 3-day SMA.
  • Exponential Moving Average (EMA):
    • Use dataframe['column_name'].ewm(span=3, adjust=False).mean() for a 3-day EMA.
  • Centered Moving Average:
    • Use dataframe['column_name'].rolling(window=3, center=True).mean() for a centered moving average.
• Visualizing Moving Averages:
  • Plot both raw data and moving averages to compare trends and fluctuations.

Speakers or Sources Featured

The video features a single speaker who presents the content in Arabic, providing explanations and demonstrations of the concepts discussed. Specific names are not mentioned in the subtitles.

Notable Quotes

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Category

Educational

Video