Time Series Analysis
Decoding the patterns of time.
Financial data is inherently sequential. Time Series Analysis involves techniques to analyze a sequence of data points collected over an interval of time. It aims to extract meaningful statistics and characteristics to forecast future values.
Key Components
Decomposition of Time Series
- Trend: Long-term direction.
- Seasonality: Recurring patterns (e.g., retail in Q4).
- Noise: Random variation.
Stationarity
Most statistical tests assume data is 'stationary' (mean and variance don't change over time). Financial data is rarely stationary (prices trend), so differencing (using returns instead of prices) is often required.
Key Takeaways
Essential for algorithmic trading.
ARIMA and GARCH are common models.
Past performance is not indicative of future results.
Time Series Analysis
Decoding the patterns of time.
Financial data is inherently sequential. Time Series Analysis involves techniques to analyze a sequence of data points collected over an interval of time. It aims to extract meaningful statistics and characteristics to forecast future values.
Table of Contents
Key Components
Decomposition of Time Series
- Trend: Long-term direction.
- Seasonality: Recurring patterns (e.g., retail in Q4).
- Noise: Random variation.
Stationarity
Most statistical tests assume data is 'stationary' (mean and variance don't change over time). Financial data is rarely stationary (prices trend), so differencing (using returns instead of prices) is often required.
Key Takeaways
Essential for algorithmic trading.
ARIMA and GARCH are common models.
Past performance is not indicative of future results.
Apply This Knowledge
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