_arima-modeling-and-forecasting-124-time-series-in-python-part-2.jpg "ARIMA modeling and forecasting | Time Series in Python Part 2 from ar in python Video Screenshot Preview")
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⏲ Duration: 7 min 39 sec
✓ Published: 26-Apr-2019
✓ Published: 26-Apr-2019
Description:
In part 2 of this video series, learn how to build an ARIMA time series model using Python’s statsmodels package and predict or forecast N timestamps ahead into the future. Now that we have differenced our data to make it more stationary, we need to determine the Autoregressive (AR) and Moving Average (MA) terms in our model. To determine this, we look at the Autocorrelation Function plot and Partial Autocorrelation Function plot. This series is considered for intermediate and advanced users.n
In part 2 of this video series, learn how to build an ARIMA time series model using Python’s statsmodels package and predict or forecast N timestamps ahead into the future. Now that we have differenced our data to make it more stationary, we need to determine the Autoregressive (AR) and Moving Average (MA) terms in our model. To determine this, we look at the Autocorrelation Function plot and Partial Autocorrelation Function plot. This series is considered for intermediate and advanced users.n
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