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Practical Time Series Analysis: Forecasting for Real-World Data
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Practical Time Series Analysis: Forecasting for Real-World Data

Gain the skills to analyze data trends and create impactful forecasts through this advanced time series analytics project.

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Apply now
Mondays
 at
6:00
P.M.
 ET /
3:00
P.M.
PT
8 weeks, 2-3 hours per week
Expert
No experience required
No experience required
Some experience required
Degree and experience required

Description

In this Build Project, you will learn to tackle the challenge of Time Series data and forecasting.

This project will immerse you in the dynamic world of temporal data, where you'll learn to identify patterns, trends, and seasonal variations using real-world datasets. Your mission is to apply advanced modeling techniques to make accurate predictions and informed decisions.

You will explore advanced modeling techniques such as ARIMA, SARIMA, and SARIMAX and gain practical experience with time series data, applying these methods effectively. Additionally, you will engage in delivering a comprehensive time series data analysis presentation.

Session timeline

  • Applications open
    May 1, 2025
  • Application deadline
    June 10, 2025
  • Project start date
    Week of July 8, 2024
    Week of
    July 7, 2025
  • Project end date
    Week of

What you will learn

By the end of the project, students will be able to:

Efficiently address missing values, outliers, and noise to ensure data integrity.

Detect and analyze anomalies and trends for proactive decision-making.

Skillfully implement and optimize forecasting models like ARIMA, SARIMA, and SARIMAX.

Assess model accuracy using metrics such as MAE, RMSE, and MAPE for reliable validation.

Project workshops

1
Workshop 1: Introductions and Understanding of Time Series Data
2
Workshop 2: Time Series Data Preprocessing and Exploratory Data Analysis
3
Workshop 3: Statistical Methods for Time Series Analysis
4
Workshop 4: Understanding Machine Learning Approaches
5
Workshop 5: Time Series Modeling Techniques: ARIMA - 1
6
Workshop 6: Time Series Modeling Techniques: ARIMA - 2
7
Workshop 7: Time Series Modeling Techniques: SARIMAX
8
Workshop 8: Model Evaluation and Presentation

Prerequisites

For students to be successful in this project, they will need:

Proficiency in Python programming and libraries such as pandas, NumPy, and Scikit-Learn for data manipulation and analysis.

Skills in using data visualization tools (e.g., matplotlib, seaborn) to create insightful visual representations of data.

Some existing exposure to basic statistical concepts (mean, median, mode, quartiles, etc.)

Basic Knowledge of Machine learning models: Familiarity with supervised and unsupervised learning, along with the concept of training and testing, provides a head start

Strong oral and written communication skills, with a desire to discuss, and share your thoughts with others

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