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PROJECT
Time Series Forecasting with Prophet in Python
Learn to perform exploratory data analysis on the time series dataset, create interactive graphs using Plotly, and forecast the future values using the Prophet library.
You will learn to:
Sample and transform data.
Create interactive graphs using Plotly in Python.
Create and interpret the OHLC and Candlestick graphs used in finance.
Forecast time series dataset using the Prophet model.
Skills
Data Visualisation
Time Series Analysis
Data Analysis
Prerequisites
Intermediate knowledge of Python
Intermediate knowledge of interactive graphs in Plotly
Basic knowledge of the Prophet model
Technologies
Python
Plotly
Pandas
Prophet
Project Description
Prophet is a powerful time series forecasting library for Python developed by Facebook's Core Data Science team. It is designed to handle time series datasets with multiple seasonalities, changing trends, and holiday effects. Prophet is built on top of a decomposable time series model with three main components—trend, seasonality, and holidays.
In this project, we'll cover the following:
Exploratory data analysis (EDA) of the Twitter stock price data.
Interactive plots for data analysis using Plotly.
The Prophet model in Python for time series forecasting of the Twitter stock price data.
Project Tasks
1
Data Loading
Task 0: Getting Started
Task 1: Import the Necessary Modules
Task 2: Load the Dataset
2
Data Visualization
Task 3: Plot the Dataset
Task 4: Comparison of Volumes for Each Year
Task 5: Create an OHLC Chart
Task 6: Take a Closer Look at the OHLC Chart
Task 7: Visualize the Moving Average
3
Time Series Prediction in Prophet
Task 8: Predict the Future Stocks
Task 9: Plot the Predicted Values
Task 10: Forecast Monthly Stock Data
Task 11: Evaluate the Model
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.