/TimeSeries

Time Series Analysis using R

TimeSeries

Time Series Analysis using R https://www.youtube.com/watch?v=wNB8AgZPFLU

WHY TIME SERIES

  • In Time series we only deal with the one Variable that will dependent on the Time
  • Where as other algorithms such as the Logistics Regression or Linear Regression they deal with the 2 Variables they are dependent and the independent variables.

What is Time Series

  • A time series is a series of the data points indexed in timed oreder.
  • Most commanly the Time series is a sequence taken at successive equally spaced points in time.
  • Time series Forecasting is the use of model to predict future values based on previously observed values.

When Not to use the time series

  • when the values are constant.
  • If the data is non stationary.

What is Stationary

  • The mean shoud be constant according to the time.
  • The Variance should be equall at differnt time intervals.
  • The co-variance should also be equall

Components of the Time Series

  1. General Trend

  2. Seasonal

  3. Irreguar Fluctuations

  4. General Trend - A General Direction in which the trend is changing. []

  5. Seasonal - A peak or dip which is sean in the time interval.

  6. Iregular Fluctutation - The un controlled situtation which aries due to which the value change. Eg: Flights cancelled due to Fog

Timeseries models

ARIMA

  • AR - Auto Regression(p)
  • MA - Moving Average(q)
  • I - Integration(d)
  • we needto get the p,q,d values from the ACF Graph
  • ACF means Auto co-relation function graph
acf(diff(log(AirPassengers))) # Determines the value of the q
pacf(diff(log(AirPassengers))) # Determines the value of the p
d value are by default 1, they change on how many times you to differanation