Perform Time series modelling using Facebook Prophet

Perform Time series modelling using Facebook Prophet

In this project, we are going to talk about Time Series Forecasting to predict the electricity requirement for a particular house using Prophet.

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What will you learn

Understanding the problem statement
Importing the necessary libraries and understanding its use
Importing the dataset directly from github
Performing basic EDA and checking for the null values
Filling the null values using appropriate methods
Finding median, average and merging the data
Feature engineering with the date
Plotting time-series graphs for visualization
Drawing a heatmap with the numeric values using Seaborn
Finding lag and lead of a time series
Using groupby function for combined analysis of variables
Differentiating a time series
Performing train_test_split to divide the dataset into train and test
Using r2_score and mean_absolute_error as evaluation metrics
Using Adaboost Regressor for making predictions
Applying the ARIMA time series model for training and making predictions
Applying Facebook Prophet model for making predictions
Visualizing the result using graphs
Selecting the best model and making the final predictions

Project Description

There are various methods to perform time series forecasting. Traditionally people have used AR, MA or ARIMA based models to perform forecasting. Prophet is an open source forecasting tool built by Facebook. It can be used for time series modeling and forecasting trends in the future. The advantage of using Prophet over traditional libraries is that one does not need to know the technicalities of time series, domain knowledge is not really required to do time series forecasting. In this Hackerday we are going to use Prophet vs other methods to do the benchmarking.

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Curriculum For This Mini Project

Data Set
02m
Introduction to Prophet
02m
Installation Steps
00m
Why Prophet
11m
Features of Prophet
09m
How Prophet works
03m
Import Data Set
04m
Data Transformation
03m
Calling Prophet
03m
Forecasting using Prophet
11m
Importing Libraries
02m
Load new Data Set
06m
Basic Statistics
06m
Feature Engineering
03m
Visualization
12m
Lag Calculation
02m
Train Test Split
02m
Linear Model
04m
Prediction
02m
Initialization
06m
Stationarity Test
05m
Decompose Time Series
02m
Arima Model
03m
Using Prophet
04m
Using Keras
08m
Conclusion
01m