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.
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What will you learn

What is a Time Series?
Visualizing a time series plot
Data used and understanding the data
Extract the trend, seasonality and random terms from the model
How to decompose a time series
How to identify autocorrelation and partial autocorrelation
How to detrend a time series
Understanding ARIMA
Understanding SARIMA
Understanding ETS
Understanding Holt Exponential Smoothing
Understanding Neural network
Understanding VAR
Other potential approaches like FBProphet, LSTM and so on.

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.




To predict the stock price



Data Overview

The data is from the EU Stock market with the following columns with a time index.

  1. DAX    - Germany DAX Stock index
  2. SMI     - Switzerland SMI Stock index
  3. CAC   - France CAC Stock index
  4. FTSE - UK Stock index



Tech Stack

  • Language used : R
  • Libraries used : FitAR, tseries, forecast, neuralnet and so on.





  1. 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

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Curriculum For This Mini Project

Business context and objective
Translating the problem into a data science approach
Data importing and understanding
Time series decomposition
Data stationary
Understanding ARIMA model
Understanding evaluation parameters
Building tuning evaluating ARIMA model
Understanding holt-winters method
Implementing holt-winter model
Understanding neural networks
Implementing neural network model
Understanding VAR algorithm
Implementing VAR model
Accuracy comparison
Other potential approaches FB prophet
FB prophet user manual walkthrough
Other potential approaches ML regression
Other potential approaches DL LSTM

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