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

1. Forecasting Projects

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

This time series project is a machine-learning model for stock market Prediction. Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange

2. Time Series Forecasting with Facebook Prophet and Python

In this tutorial, you’ll learn how to use time series forecasting to produce a forecast. In just a couple of minutes, you’ll be able to preprocess your dataset using Pandas and forecast over a number of time periods using Facebook Prophet.

In this tutorial you’ll learn how to:

1. Preparing Data for Time Series FC
2. Training Prophet Time Series Models
3. Making forecast predictions

3. Forecasting Future Sales Using ARIMA and SARIMAX

In this project, you will predict future sales of Perrin Freres monthly champagne sales using ARIMA and Seasonal ARIMA models using Python.

In this tutorial you’ll learn how to:

• Visualize the Time Series Data
• Make the time series data stationary
• Plot the Correlation and AutoCorrelation Charts
• Construct the ARIMA Model or Seasonal ARIMA based on the data
• Use the model to make predictions

4. Forecasting Weather with Neural Prophet and Python

In this project, you will use the Neural Prophet to forecast the weather. Neural Prophet is a relatively new library that uses Facebook's Prophet time series forecasting package and a Pytorch AR-Net model to produce highly accurate time series forecasts quickly. This makes it easy to build your own weather forecasts and forecast with any other time series-style data.

In this video you’ll learn how to:

1. Preprocess a weather dataset from Kaggle using Pandas
2. Train a time series forecasting model to predict temperature using Neural Prophet
3. Forecast temperature into the future using the trained model

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

In this project, you will walk through a time series forecasting example in Python using a machine learning model XGBoost to predict energy consumption with Python. You will walk through this project in a kaggle notebook that you can copy and explore while watching.

6. Build A Stock Prediction Web App In Python

In this tutorial, you will build a stock prediction web app in Python using streamlit, Yahoo Finance, and Facebook Prophet.

7. Time Series Forecasting with PyCaret Regression Module

In this tutorial, you walk through a hands-on tutorial to use PyCaret to forecast and predict future values.

2. Time Series Analysis Projects

2.1. Hourly Energy Data Time Series Analysis

In this project, you will deal with the basics of energy (electricity) consumption, how to import, resample, and merge datasets gathered from different sources, EDA, and draw some basic inferences from San Diego’s energy consumption data.

2.2. Stock Market Performance Analysis using Python

In this project, you will walk through the task of Stock Market Performance Analysis using Python. Stock Market Performance Analysis involves calculating moving averages, measuring volatility, conducting correlation analysis, and analyzing various aspects of the stock market to gain a deeper understanding of the factors that affect stock prices and the relationships between the stock prices of different companies. If you want to learn how to analyze the stock market performance for a given period, this project is for you.

2.3. Exploratory Data Analysis for Time Series Data using PyCaret

In this project, you will learn how to use Pycaret for exploratory data analysis for time series data.

3. Anomaly Detection Projects

3.1. Anomaly Detection via PyCaret

In this project-based course you will learn how to perform anomaly detection, its importance in machine learning, set up PyCaret anomaly detection, create, visualize & compare anomaly detection algorithms all this with just a few lines of code.

3.2. An End-to-End Unsupervised Anomaly Detection

In this project, you will build a time-series outlier detection using Facebook’s Prophet for Batch Processing

3.3. Time Series Anomaly Detection with PyFBAD

The typical flow of a machine learning project starts with reading the data, followed by some preprocessing, training, testing, visualization, and sharing the results with the notification system.

Of course, all the steps can be easily done with the help of various open-source libraries. However, in some task-specific cases, such as anomaly detection in time series data, reducing the number of library and hard-coded steps would be more beneficial for explainability. The pyfbad library has been developed for that reason.

In this project, you will pyfbad library to detect anomaly detection.

If you like the article and would like to support me, make sure to:

• 👏 Clap for the story (50 claps) to help this article be featured
• Subscribe to To Data & Beyond Newsletter
• Follow me on Medium
• 📰 View more content on my medium profile
• 🔔 Follow Me: LinkedIn |Youtube | GitHub | Twitter

Looking to start a career in data science and AI do not know how. I offer data science mentoring sessions and long-term career mentoring:

• Mentoring sessions: https://lnkd.in/dXeg3KPW
• Long-term mentoring: https://lnkd.in/dtdUYBrM