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SARIMA_​Example_​TSA_​Book

SARIMA Temperature Forecasting

This workflow demonstrates how the SARIMA components can be used to generate forecasts. In this case for hourly temperature data.

Observe the oscillating pattern in the ACF Plot in the component view. We notice that it has period 24 and we will use this as our s value in the SARIMA. It is the Seasonal Period. Our daily pattern. First we remove the 24 hour seasonal pattern with the remove seasonality component, then inspect the ACF and PACF on this differenced series to determine p,d,P, and Q hyper-parameters.Remember we look for how long the ACF takes to decay to choose q, how long the PACF plot takes to decay for p, and count seasonal spikes (at s=24) to choose Q and P from the ACF and PACF respectively. (1,0,6)(2,1,0)245 dayforecastOverlay forecastand true valuesPlot DataRead TemperatureData for LAConvert Kelvinto Celcius SARIMA Learner SARIMA Predictor Partitioning Visualize Forecast Line Plot Table Reader Inspect Seasonality Math Formula Remove Seasonality Inspect Seasonality Observe the oscillating pattern in the ACF Plot in the component view. We notice that it has period 24 and we will use this as our s value in the SARIMA. It is the Seasonal Period. Our daily pattern. First we remove the 24 hour seasonal pattern with the remove seasonality component, then inspect the ACF and PACF on this differenced series to determine p,d,P, and Q hyper-parameters.Remember we look for how long the ACF takes to decay to choose q, how long the PACF plot takes to decay for p, and count seasonal spikes (at s=24) to choose Q and P from the ACF and PACF respectively. (1,0,6)(2,1,0)245 dayforecastOverlay forecastand true valuesPlot DataRead TemperatureData for LAConvert Kelvinto CelciusSARIMA Learner SARIMA Predictor Partitioning Visualize Forecast Line Plot Table Reader Inspect Seasonality Math Formula Remove Seasonality Inspect Seasonality

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