Skip to content

Using SARIMAX for Time Series Forecasting on Seasonal Data that is influenced by Exogenous variables

Notifications You must be signed in to change notification settings

hhk998402/Time-Series-Forecasting-SARIMAX

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Time-Series-Forecasting-SARIMAX

Using SARIMAX for Time Series Forecasting on Seasonal Data that is influenced by Exogenous variables

(Prepared for) Sangam 2019 - ML Hackathon by IITMAA

Data Provided: Traffic Data (refer train.csv for more)

Data description

Columns

Description

date_time

Date, time, and hour of the data that is collected in the local IST time

is_holiday

Categorical Indian national holidays combined with regional holidays

air_pollution_index

Air Quality Index (10-300)

humidity

Numeric humidity in Celcius

wind_speed

Numeric wind speed in miles per hour

wind_direction

Cardinal wind direction (0-360 degree)

visibility_in_miles

Visibility of distance in miles

dew_point

Numeric dew point in Celcius

temperature

Numeric average temperature in Kelvin

rain_p_h

Numeric amount in mm of rain that occurred in the hour

snow_p_h

Numeric amount in mm of snow that occurred in the hour

clouds_all

Numeric percentage of cloud cover

weather_type

Categorical short textual description of the current weather

weather_description

Categorical longer textual description of the current weather

traffic_volume

Numeric hourly traffic volume bound in a specific direction

The traffic_volume attribute has to be forecasted on the basis of the time series data provided, taking the exogenous variables into account

Approach used: SARIMAX (Seasonal Autoregressive Integrated Moving Average with eXogeneous variables)

Reason: The data provided is seasonal, and it is a time series data with multiple exogeneous variables influencing the result. Hence, the optimal statistical model that can be applied to this task is SARIMAX

Main Modules Used:
  • statsmodel package in Python

About

Using SARIMAX for Time Series Forecasting on Seasonal Data that is influenced by Exogenous variables

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published