Time series analysis is a statistical technique used to analyze data points collected over time. It's essential for understanding trends, seasonal patterns, and forecasting future values. Here's a breakdown of key concepts and steps:
What is a Time Series? 📅
A time series is a sequence of data points ordered chronologically. For example:
- Daily stock prices
- Monthly temperature readings
- Annual sales figures
Core Concepts 🔍
- Trend - Long-term direction (↑/↓) in data
- Seasonality - Regular patterns within fixed time cycles (e.g., yearly, monthly)
- Cyclical Variations - Fluctuations tied to economic or business cycles
- Irregular Components - Random, unpredictable events
Analysis Steps 🧱
- Data Collection
- Visual Inspection (plotting data)
- Statistical Testing (for trends/seasonality)
- Model Selection (ARIMA, Exponential Smoothing, etc.)
- Forecasting
Tools & Resources 🛠️
- Python Libraries for analysis
- R Programming guides
- Interactive Examples
For deeper learning, explore our Time Series Forecasting Guide next!