Time Series Prediction of Temperature in Pune Using Seasonal ARIMA Model

EasyChair Preprint 6701

Abstract

In this paper, an attempt has been made to develop a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict temperature using past data of Pune, Maharashtra. The dataset from 2009 to 2020 has been taken for analysis. When trend and seasonality is present in a time series, instead of decomposing it manually to fit an ARIMA model, another very popular method is to use the seasonal autoregressive integrated moving average (SARIMA) model which is a generalization of an ARIMA model. Time series lately is becoming very popular, a reason for that is decreasing hardware's cost and capability of processing. The model can be used to calculate what Patterns should be in the coming year. Quantify the effects of sudden changes or disruptions in the system. The seasonal ARIMA model is implemented by running Python 3.7.4 on Jupyter Notebook and using the package matplotlib 3.2.1 for data visualization. The goodness of fit of the model was tested against standardized residuals, the autocorrelation function, and the partial autocorrelation function. We discover that SARIMA (1,1,1)(1,1,1)12 can represent very well in the data behavior. We obtained MAE of 0.60850 and RMSE of 0.76233 for SARIMA model. According to the model diagnostics, the model was reliable for predicting temperature.

Keyphrases: ARIMA, SARIMA, Temperature, prediction

@booklet{EasyChair:6701,
  author    = {Aarati Gangshetty and Gurpreet Kaur},
  title     = {Time Series Prediction of Temperature in Pune Using Seasonal ARIMA Model},
  howpublished = {EasyChair Preprint 6701},
  year      = {EasyChair, 2021}}