Time Series Analysis Project in R on Stock Market forecasting
Time Series Analysis Project in R on Stock Market forecasting
In this time series project, you will build a model to predict the stock prices and identify the best time series forecasting model that gives reliable and authentic results for decision making.
Videos
Each project comes with 2-5 hours of micro-videos explaining the solution.
Code & Dataset
Get access to 102+ solved projects with iPython notebooks and datasets.
Project Experience
Add project experience to your Linkedin/Github profiles.
Customer Love
Prasanna Lakshmi T
Initially, I was unaware of how this would cater to my career needs. But when I stumbled through the reviews given on the website. I went through many of them and found them all positive. I would... Read More
Arvind Sodhi
I have extensive experience in data management and data processing. Over the past few years I saw the data management technology transition into the Big Data ecosystem and I needed to follow suit. I... Read More
SUBHABRATA BISWAS
The project orientation is very much unique and it helps to understand the real time scenarios most of the industries are dealing with. And there is no limit, one can go through as many projects... Read More
Nathan Elbert
This was great. The use of Jupyter was great. Prior to learning Python I was a self taught SQL user with advanced skills. I hold a Bachelors in Finance and have 5 years of business experience.. I... Read More
Camille St. Omer
I came to the platform with no experience and now I am knowledgeable in Machine Learning with Python. No easy thing I must say, the sessions are challenging and go to the depths. I looked at graduate... Read More
Swati Patra
I have 11 years of experience and work with IBM. My domain is Travel, Hospitality and Banking - both sectors process lots of data. The way the projects were set up and the mentors' explanation was... Read More
What will you learn
Project Description
Business Objective
The main objective of this problem is to forecast stock market data using traditional and advanced state of the art algorithms. EuroStockMarket Dataset contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted. The stock market can have a huge impact on the people and the countries economy as a whole and hence predicting the prices of stock can reduce the risk of loss and maximize the profit.
Aim
To predict the stock price
Data Overview
The data is from the EU Stock market with the following columns with a time index.
- DAX - Germany DAX Stock index
- SMI - Switzerland SMI Stock index
- CAC - France CAC Stock index
- FTSE - UK Stock index
Tech Stack
- Language used : R
- Libraries used : FitAR, tseries, forecast, neuralnet and so on.
Approach
- Data cleaning / Pre-processing (outlier/missing values/categorical) -
- Extract the trend, seasonality and random terms from the model
- Decompose the time series
- Identify autocorrelation and partial autocorrelation
- Detrend a time series
2. Holt winter Method
- Understanding holt winter method
- Building model and tuning
- Evaluation parameters understanding
- Evaluation
3. ARIMA method
- Understanding ARIMA method
- Building model and tuning
- Evaluation
4. VAR method
- Understanding VAR method
- Building model and tuning
- Evaluation
5. Neural Network method
- Understanding neural network method
- Building model and tuning
- Evaluation
6. Finalise Model
- Which model will you finalize on and based on what metrics
7. Comparison and other potential approaches
8. Deployment
- Making production ready code
New Projects
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
Hive Project -Learn to write a Hive program to find the first unique URL, given 'n' number of URL's.
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.
In this deep learning project, you will build your own face recognition system in Python using OpenCV and FaceNet by extracting features from an image of a person's face.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
Curriculum For This Mini Project
Latest Blogs
100 Kafka Interview Questions and Answers for 2021
50 Cloud Computing Interview Questions and Answers for 2021
Apache Kafka Architecture and Its Components-The A-Z Guide
15 Neural Network Projects Ideas for Beginners to Practice 2021
15 TensorFlow Projects Ideas for Beginners to Practice in 2021
Kafka vs RabbitMQ - A Head-to-Head Comparison for 2021
Data Engineer vs Data Scientist- The Differences You Must Know
