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