In this liveProject series, you play the part of a consultant whose client list includes prestigious financial institutions. One of them has hired you to improve the accuracy of their model for stock price prediction. Since most financial data include a time dimension, time series analysis is a hot topic in finance—and an excellent application for this type of modeling. To help your client achieve their goal, you’ll perform the essential steps in time series modeling including data preparation as well as model performance analysis and comparison. First you’ll run classical moving average (simple MA and exponential MA), autoregressive (AR), and autoregressive integrated moving average (ARIMA) models. Then you’ll boost their performance by applying a hybrid approach that combines these models with deep learning models, leveraging the strengths of both. You’ll also learn to visualize results and evaluate performance.
Imagine you’re a consultant with a number of prestigious financial institutions on your client list. One of them has hired you to increase the accuracy of their model for predicting stock prices. You rise to the challenge, deciding on some classical time series models. But before you can propose a reliable model, you must decompose and examine the time series data in order to understand its pattern. Once you have a firm grasp on the data’s peculiarities, you’ll be ready to run the time series modeling.
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).
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.
Now that you’ve prepared the data for the deep learning models, you’re ready to apply the hybrid model, which leverages the strengths of both the classical and deep learning models. In this liveProject, you’ll focus on the essential steps for running a time series analysis. You’ll start with ensuring the data you prepared is ready for processing with the deep learning models. Next, you’ll run the RNN and LSTM for the separate datasets. Finally, you’ll evaluate the performance of your models.
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 following:
In this liveProject series, you’ll learn to run classical time series models, prepare data for modeling, employ a hybrid approach to time series modeling for better performance, visualize results, and evaluate performance.
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