Time Series Analysis and Forecasting

Time series analysis and forecasting is a specialized discipline within data science and computer science focused on analyzing and modeling data points collected in chronological order. The process involves identifying underlying patterns in historical data—such as trends, seasonality, and cyclical behavior—to understand its structure and anomalies. Building on this analysis, forecasting techniques, which range from classical statistical models like ARIMA to advanced machine learning algorithms like LSTMs, are then applied to predict future values, making it a critical tool for applications like financial market prediction, demand planning, and resource allocation.

1. Introduction to Time Series Data
   1. Defining Time Series
      1. Basic definition and characteristics
      2. Distinction from cross-sectional data
      3. Distinction from panel data
      4. Time as the index variable
   2. Types of Time Series
      1. Univariate time series
      2. Multivariate time series
      3. Panel time series
   3. Characteristics of Time Series Data
      1. Time-ordered observations
      2. Temporal dependence
      3. Sequential nature of data
      4. Non-independence of observations
   4. Time Intervals and Frequency
      1. Regular time intervals
      2. Irregular time intervals
      3. High-frequency data
      4. Low-frequency data
      5. Common frequencies
         1. Daily
         2. Weekly
         3. Monthly
         4. Quarterly
         5. Annual
   5. Applications of Time Series Analysis
      1. Economic forecasting
      2. Financial market analysis
      3. Demand and sales forecasting
      4. Resource management
      5. Process and quality control
      6. Environmental and climate studies
      7. Healthcare and epidemiology
      8. Energy consumption forecasting
      9. Population dynamics