Time Series Analysis:
Definition:Time series analysis involves studying and modeling data collected over time to identify patterns, trends, and make predictions.
Methods:
1. Moving Averages: Smoothens fluctuations over time.
2. Exponential Smoothing: Assigns varying weights to different data points.
3. ARIMA (AutoRegressive Integrated Moving Average): Models temporal dependencies and trends.
4. Seasonal Decomposition of Time Series (STL): Breaks down data into trend, seasonality, and remainder components.
5. Prophet: Developed by Facebook for forecasting with daily observations and multiple seasonality.
Uses:
– Forecasting: Predict future values based on historical patterns.
– Anomaly Detection: Identify unusual patterns or events.
– Trend Analysis: Understand long-term developments.
– Financial Market Analysis: Predict stock prices and market trends.
– Demand Planning: Optimize inventory based on future demand.
– Economic Indicators: Analyze and predict economic trends over time.