TimeSeriesAnalysis: WEAT ETF Stock Price History

This project delves into the stock price history of the WEAT ETF, employing various analytical techniques and visualizations to explore the company's past performance. The goal is to gain insights into the stock's historical behavior and make future predictions using time series analysis.

Overview

The notebook begins with an introduction to the WEAT ETF company's stock history, setting the stage for a comprehensive analysis. The project aims not only to review past performance but also to forecast future trends based on historical data.

Data

The dataset provided encompasses several key financial indicators related to the WEAT ETF's stock, including:

Date: The specific day of the transaction.
Price: The stock's closing price at the end of the trading day.
Open: The price at which the stock first traded upon the opening of the exchange.
High: The highest price at which the stock traded during the trading day.
Low: The lowest price at which the stock traded during the trading day.
Volume: The total number of shares traded during the trading day.
Change %: The percentage change in the closing price from the previous day's close.

Methodology

The analysis begins with importing the dataset using the Pandas library, followed by an examination of basic statistical properties. Subsequent sections (to be detailed further) likely involve:

Data cleaning and preprocessing
Exploratory data analysis (EDA)
Time series decomposition
Trend analysis
Seasonality examination
Forecasting future stock prices

Tools and Libraries

This project leverages Python for data analysis, with a significant focus on the following libraries:

Pandas: For data manipulation and analysis.
NumPy: For numerical computing.
Matplotlib/Seaborn: For data visualization.
Additional libraries may include statsmodels for statistical modeling and scikit-learn for machine learning applications.

Conclusion