Choosing the right Time Series Forecasting Methods

There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.

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What will you learn

  • What is a Time Series?

  • How to create a time series?

  • Visualizing a time series plot

  • How to convert a multiplicative time series into an additive time series

  • Logarithmic transformation its significance and its use

  • Assumptions of a time series forecasting model

  • To extract the trend, seasonality and random terms from the model

  • How to decompose a time series

  • Using standard regression-based methods to do forecast

  • How to identify autocorrelation and partial autocorrelation

  • How to de-trend a time series

  • Augmented Dickey-Fuller Test (ADF test for stationarity)

  • When and how to use the differencing of a time series

  • Double exponential smoothing model

  • MAPE metric to compare the accuracy of any time series model

  • What is the ARIMA model and steps to initialize model

  • Applying ARIMA model for forcasting

Project Description

In this machine learning project, we will be taking open source datasets that are publicly available and will be discussing various methods/techniques of performing time series forecasting. We will discuss about the traditional methods such as holt-winters method, Autoregressive integrated moving average method, exponential smoothing methods, as well we will also be comparing the modern methods of performing forecasting using neural network based models.

 

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Curriculum For This Mini Project

 
  What is Time Series
04m
  How to create Time Series in R
05m
  Components of Time Series
03m
  Assumptions of Time Series Forecasting
05m
  Investigate components of Time Series
02m
  Example - Stock Market Dataset
01m
  Decompose method - Additive/Multiplicative Component
03m
  STL method
02m
  Plot graph for Decompose and STL function
01m
  How to do Time Series Forecasting
08m
  Create Time Series Lag
10m
  Add more Lag variables
02m
  Identify AutoCorrelation
11m
  Partial Autocorrelation
01m
  DeTrend a Time Series using Johnson method
07m
  Deseasonalize Time Series
05m
  Seasonality Plot
03m
  Augmented Dickey Fuller (ADF) Test
03m
  KPSS Test
02m
  Air Passenger Data - ADF Test
01m
  Differencing Method
17m
  Moving Average Method
04m
  Data Visualization
01m
  Forecast Methods
03m
  Moving Average Method
06m
  Exponential Smoothing Method
01m
  Simple Exponential Smoothing Method
06m
  Double Exponential Smoothing Method
01m
  Triple Exponential Smoothing Method
01m
  Compare accuracy of models
07m
  Generate forecast using all 3 methods
03m
  Arima - Auto Regressive Integrated Moving Average Model
17m
  Get the Dataset ready
07m
  Decompose Time Series Data
08m
  Diffrentiation
05m
  How to identify Lags
05m
  Auto Arima
15m
  Forecast
02m
  Dummy Variables & Box Cox Transformation
02m
  Neural Network based Forecasting
09m
  Hyperparameter Tuning to increase accuracy
06m
  Conclusion
01m