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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.
4.94.9

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What will you learn

  • Understanding the importance of time series
  • Understanding the mathematics of time series
  • Discussion about methods/techniques
  • Application of the models using R or Python
  • Making conclusions

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Prerequisites

  • Jupyter Notebook from Anaconda installation
  • R (3.3.3) and R-Studio (1.4) installation
  • At least 4 GB RAM Machine

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.

 

Instructors

 
Pradeepta

Curriculum For This Mini Project

 
  What is Time Series
00:04:22
  How to create Time Series in R
00:05:11
  Components of Time Series
00:03:49
  Assumptions of Time Series Forecasting
00:05:19
  Investigate components of Time Series
00:02:30
  Example - Stock Market Dataset
00:01:41
  Decompose method - Additive/Multiplicative Component
00:03:30
  STL method
00:02:36
  Plot graph for Decompose and STL function
00:01:33
  How to do Time Series Forecasting
00:08:01
  Create Time Series Lag
00:10:52
  Add more Lag variables
00:02:03
  Identify AutoCorrelation
00:11:18
  Partial Autocorrelation
00:01:57
  DeTrend a Time Series using Johnson method
00:07:47
  Deseasonalize Time Series
00:05:21
  Seasonality Plot
00:03:00
  Augmented Dickey Fuller (ADF) Test
00:03:29
  KPSS Test
00:02:29
  Air Passenger Data - ADF Test
00:01:31
  Differencing Method
00:17:53
  Moving Average Method
00:04:22
  Data Visualization
00:01:47
  Forecast Methods
00:03:22
  Moving Average Method
00:06:59
  Exponential Smoothing Method
00:01:11
  Simple Exponential Smoothing Method
00:06:34
  Double Exponential Smoothing Method
00:01:17
  Triple Exponential Smoothing Method
00:01:25
  Compare accuracy of models
00:07:03
  Generate forecast using all 3 methods
00:03:50
  Arima - Auto Regressive Integrated Moving Average Model
00:17:14
  Get the Dataset ready
00:07:16
  Decompose Time Series Data
00:08:09
  Diffrentiation
00:05:41
  How to identify Lags
00:05:38
  Auto Arima
00:15:50
  Forecast
00:02:33
  Dummy Variables & Box Cox Transformation
00:02:20
  Neural Network based Forecasting
00:09:42
  Hyperparameter Tuning to increase accuracy
00:06:12
  Conclusion
00:01:31