A Practical Approach to Timeseries Forecasting Using Python - Moving Average and ARMA

The video tutorial explains the moving average model, which forecasts the next step in a sequence by averaging a window of prior observations. It covers the model's parameters, such as the number of moving average terms (Q) and lagged forecast errors. An example involving birthday pastries illustrates the model's application. The tutorial also discusses calculating ACF values for model analysis and introduces the ARMA model, which combines auto regression and moving average. The ARMA model uses previous lags and residuals for forecasting, considering both PACF and ACF graphs. The tutorial concludes with a note on the ARIMA model, which will be discussed in the next section.

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