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Gain the skills to analyze data trends and create impactful forecasts through this advanced time series analytics project.
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.
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.
Get to know the Build Fellow and explore basic understanding of Time Series Data. This will set students up to identify the open source dataset that they are interested in working for their projects.
In this workshop, you will work with raw time series data to address missing values and handle outliers. You will also perform Exploratory Data Analysis (EDA) by creating visualizations to identify trends and anomalies. By the end, you will have a cleaned dataset and initial insights to build upon in subsequent workshops.
In this workshop, you will apply statistical techniques to analyze time series data. You will generate and interpret ACF and PACF plots to uncover patterns and dependencies, and decompose the data into trend, seasonal, and residual components for deeper insights.
In this workshop, you will focus on applying regression-based models, specifically linear regression, to time series analysis. You will learn to preprocess data, build models, and evaluate their performance to ensure accurate forecasting.
In this workshop, you will grasp the fundamentals of ARIMA (Autoregressive, Integrated, Moving Average) models, and prepare data for modeling. Learn to preprocess and transform time series data to apply ARIMA models to real-world datasets and evaluate their performance to make informed predictions and decisions.
In this workshop, you will be using fundamental knowledge on ARIMA build model implementation, and result interpretation. Learn to develop and apply ARIMA models to real-world datasets and evaluate their performance to make informed predictions and decisions.
In this workshop, you will develop a comprehensive understanding of the SARIMAX model, including its seasonal components and exogenous variables. Learn to identify and incorporate seasonal patterns and external factors, and gain hands-on experience in building, implementing, and evaluating SARIMAX models using real-world datasets for informed decision-making. Compare SARIMAX vs SARIMA vs ARIMA model to understand the differences.
In this workshop, you will Integrate your findings into a presentation and present them to the Build Fellow and other students in the group session.
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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