Time Series Visualization

[sharayuanuse]

Is your feature request related to a problem? Please describe.

Understanding time series data can often be challenging without effective visualizations and analysis. Stakeholders may struggle to identify trends, seasonality, and key insights from raw data alone, leading to suboptimal decision-making. Additionally, existing tools may not offer sufficient customization or interactivity to effectively convey complex patterns within time series data.

Describe the solution you'd like
A robust time series visualization tool that includes:

• Exploratory Data Analysis (EDA): Summarizing the dataset's characteristics to provide initial insights into its structure and distribution.
• Time Series Visualization: Employing various plotting techniques to visualize trends and seasonality.
• Trend Analysis: Identifying long-term trends and seasonal patterns within the dataset.
• Reporting: Generating a detailed report that encapsulates findings, visualizations, and statistical summaries.

Describe alternatives you've considered

• Basic static plots, though they lack interactivity and engagement.
• Using third-party visualization libraries like Plotly or Matplotlib, but these may require more customization efforts and lack out-of-the-box features like automated reports.
• Manually creating reports from analysis, which is time-consuming and prone to error.

Approach to be followed (optional)

• Data Loading: Will utilize Pandas to load the dataset and perform initial data checks, including handling missing values and examining data types to ensure data integrity.
• Exploratory Data Analysis (EDA): Will conduct EDA to investigate data distribution, outliers, and correlations, providing a solid foundation for further analysis.
• Visualizations:
  • Autocorrelation Plot: Will analyze the correlation of the time series with its own lagged values to identify any periodic patterns.
  • Moving Average Plot: Will visualize the moving averages to smooth out fluctuations and highlight trends.
  • Exponential Smoothing Plot: Will implement exponential smoothing techniques to assess trends while accounting for noise in the data.
  • Seasonal Plots: Will generate seasonal decomposition plots to showcase seasonal variations and trends across different time frames.
  • Trend Analysis: Will develop detailed trend analysis plots to highlight significant trends within the data over time.
• Report Generation: Will compile all findings, visualizations, and insights into a comprehensive PDF report, offering an accessible overview of the analysis process and results.

Additional context

[github-actions]

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[github-actions]

Hello @sharayuanuse! Your issue #138 has been closed. Thank you for your contribution!