ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) Analysis:
ACF and PACF are pivotal tools in time series analysis, revealing temporal dependencies within data. ACF measures correlation between a time series and its lags, showcasing patterns in a plot with a gradual decline in correlation as lags increase. PACF, on the other hand, isolates direct relationships between a point and its lags, aiding in pinpointing specific influential lags.
Interpretation involves identifying peaks in ACF and PACF plots, indicating significant correlations and aiding in the detection of patterns or cycles. ACF is effective for identifying seasonality, while PACF helps determine autoregressive order.
In practical terms, insights from ACF and PACF analyses guide model building, contributing to parameter selection for models like ARIMA. Iterative refinement enhances model accuracy, and diagnostic checks on model residuals ensure robustness in capturing underlying patterns. ACF and PACF analyses collectively empower effective time series modeling.
https://colab.research.google.com/drive/1_oYwuN37I_K08_3nv2FO2-psxbeL7TG5?usp=sharing
The plotted graph displays a sine wave, showcasing periodic oscillations over a 2π range. The x-axis represents the input values, while the y-axis represents the corresponding sine values, providing a visual representation of this fundamental mathematical function.