Four-Project Series

prerequisites
intermediate Python • basics of Matplotlib • basics of Jupyter Notebook • basics of time series analysis • intermediate machine learning • basics of TensorFlow
skills learned
prepare data for time series modeling • run classical time series models • run deep learning models • run hybrid time series models • visualize and evaluate performance
Abdullah Karasan
4 weeks · 4-6 hours per week average · ADVANCED

### pro \$24.99 per month

• choose one free eBook per month to keep
• exclusive 50% discount on all purchases

### team

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.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

## here's what's included

Project 1 Time Series Components

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.

\$29.99 \$19.49

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).

\$29.99 \$19.49
Project 3 Deep Learning

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.

\$29.99 \$19.49
Project 4 Hybrid Model

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.

\$29.99 \$19.49

## book resources

When you start each of the projects in this series, you'll get full access to the following book for 90 days.

## team

monthly
annual
\$49.99
\$499.99
only \$41.67 per month
• five seats for your team
• choose another free product every time you renew
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## project author

Abdullah Karasan
Abdullah Karasan was born in Berlin, Germany. After studying economics and business administration, he obtained his master's degree in applied economics from the University of Michigan, Ann Arbor, and his PhD in financial mathematics from the Middle East Technical University, Ankara. He is a former Treasury employee of Turkey and currently works as a principal data scientist at Magnimind and as a lecturer at the University of Maryland, Baltimore. He has also published several papers in the field of financial data science.

## Prerequisites

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:

TOOLS:
• Intermediate Python
• Basics of Matplotlib
• Basics of TensorFlow
• Jupyter Notebook
TECHNIQUES
• Basics of time series analysis
• Intermediate machine learning
• statsmodel for running ACF, PACF plot and stationary analysis
• seasonal_decompose for detecting the time series components
• Alpha Vantage API for data extraction

## features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.