You’ve shown your expertise in successfully modeling a time series using classical models: moving average (simple MA and exponential MA), autoregressive (AR), and autoregressive integrated moving average (ARIMA). Your next step is to determine if there’s a way to boost the performance of the time series analysis. You decide you’ll apply an unconventional approach that’s increasingly popular in finance circles: a hybrid model that combines deep learning and classical approaches. For the deep learning models, you choose recurrent neural network (RNN) and long short-term memory (LSTM). In this liveProject, you’ll focus on preparing the data for the deep learning models. The good news is that in deep learning models, you avoid a long and cumbersome preprocessing stage since, unlike in many classical approaches, they are able to detect patterns almost automatically.
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 and be familiar with the following:
In this liveProject, you’ll learn to prepare data specifically for deep learning models:
geekle is based on a wordle clone.