1 min readJun 28, 2021
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Time Series Basics
Time series can have three components
- Trend
- Seasonality
- Random fluctuations (noise)
Stationary Time series
- Mean, Variance, Covariance remains constant over time (needs to remove trend, seasonal components) from time series as certain model works on Stationary time series
- To make series stationary , take log, differencing (or both), square root, log transformation
Model that requires Time series has to be stationary
- AR(p) — p is number of last lag’s data value taken in account for predicting
- MA(q) — q is number last lag’s error taken into account predicting
- ARIMA (p,d,q) — d for differencing to remove trend and seasonal effect
- SARIMA (p,d,q)(P,D,Q)s — s is frequency for seasonality
Model requires to know value of p and q that is determined by plotting
- ACF — Auto Correlation Plot (used for MA model to decide for q value, the number from which correlation cuts-off to very insignificant value)
- PCF — Partial Correlation Plot (used for AR model to decide for p value, the number from which correlation cuts-off to very insignificant value)
Models that Doesn’t assume that time series stationary
- Holt models (models around level (mean) alpha, trend (beta)) — when time series has no seasonal component
- Holt Winters model (same as above + Seasonal component (gamma)
- ARCH model (based on Variance)