Time Series Forecasting using TBATS Model

Multi seasonalities Time series forecasting model with python and R codes

Introduction

Time-series forecasting refers to the use of a model to predict future values based on previously observed values. Many researchers are familiar with time-series forecasting yet they struggle with specific types of time-series data. One such type of data is data with seasonality. There can be many types of seasonalities present (e.g., time of day, daily, weekly, monthly, yearly).

TBATS is a forecasting method to model time series data. The main aim of this is to forecast time series with complex seasonal patterns using exponential smoothing

TBATS: Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components.

Overview

In order to start forecasting, we need to first install tbats package. The following steps should be implemented to create the model:

• Partition the data into two parts(say, train_data and test_data). Train_data is used to train the model and fit the model to data. The trained model is evaluated using test_data.