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Now that you’ve detected the time series components and obtained the stationary data, you’re ready to move on to time series modeling. In this liveProject, your challenge is to determine which model will perform best for your client’s data. To do this, you’ll apply the classical moving average (simple MA and exponential MA), autoregressive (AR), and autoregressive integrated moving average (ARIMA) models. Then you’ll compare their performance using a visualization and performance metric, root mean square error (RMSE).
This liveProject is for finance practitioners and anyone interested in gaining hands-on experience with time series analysis in finance.To begin this liveProject you will need to know the basics of time series analysis, have intermediate-level machine learning knowledge, and be familiar with the following:
geekle is based on a wordle clone.