13 Guided Time Series Projects to Build Your Portfolio

Elevate Your Data Science Portfolio with These 13 Time Series Guided Projects

Time series data, which tracks values over time, is a fundamental aspect of data science, with applications ranging from finance to healthcare, energy, and beyond. Yet, mastering this field requires more than just theoretical knowledge. It demands practical experience, and that’s where these 13 guided projects come into play.

Whether you’re an aspiring data scientist taking your first steps or a seasoned pro looking to expand your skill set, this article is tailor-made for you. Our projects cover a diverse spectrum, from forecasting stock prices using deep learning techniques to predicting the weather with advanced models like Neural Prophet and harnessing machine learning to detect anomalies within time series data.

Why should you delve into these projects? For starters, they offer you a hands-on approach to mastering the intricate art of Time Series Analysis and Forecasting. You’ll gain real-world experience, hone your analytical skills, and build a portfolio that speaks volumes about your expertise.

Who should read this article? It’s for anyone passionate about data science, looking to bolster their career prospects, or simply intrigued by the dynamics of time-varying data. Whether you’re a student, a professional, or an inquisitive mind, these projects provide a pathway to becoming proficient in Time Series Analysis and Forecasting.

Table of Contents:

1. Forecasting Projects:

1.1. Stock Price Prediction And Forecasting Using Stacked LSTM- Deep Learning

1.2. Time Series Forecasting with Facebook Prophet and Python 

1.3. Forecasting Future Sales Using ARIMA and SARIMAX

1.4. Forecasting Weather with Neural Prophet and Python

1.5. Time Series Forecasting with XGBoost — Use Python and machine learning to predict energy consumption

1.6. Build A Stock Prediction Web App In Python

1.7. Time Series Forecasting with PyCaret Regression Module

2. Time Series Analysis Projects:

2.1. Hourly Energy Data Time Series Analysis

2.2. Stock Market Performance Analysis using Python

2.3. Exploratory Data Analysis for Time Series Data using PyCaret

3. Anomaly Detection Projects:

3.1. Machine Learning — Anomaly Detection via PyCaret

3.2. An End-to-End Unsupervised Anomaly Detection

3.3. Time Series Anomaly Detection with PyFBAD