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Welcome to the Weather-Forecasting-using-ARIMA wiki!

Weekly Progress

Week 1 (October 18-22 2020)

• Read research papers on weather forecasting.
• Compare the performance of all the models and choose the one with the best result.
• Search for the dataset for weather forecasting.

Week 2 (October 24-28, 2020)

• Feature Engineering from basic EDA and time series prediction's point of view.
• Data Cleaning for temperature forecasting
• Data Analysis for temperature forecasting
• Time series Prediction for temperature forecasting

Week 3 (October 31-November 4, 2020)

• Feature Engineering from basic EDA and time series prediction's point of view.
• Data Cleaning for dewpoint forecasting
• Data Analysis for dewpoint forecasting
• Time series Prediction for dewpoint forecasting

Week 4 (November 5-11, 2020)

• Modification in data preprocessing for temperature forecasting using ARIMA
• Modification in data preprocessing for dewpoint forecasting using Auto ARIMA

Week 5 (November 12-15, 2020)

• Project report

Summary

We have implemented the ARIMA model of weather forecasting on New Delhi's weather dataset. The ARIMA model is applied on New Delhi's temperature data as the data is stationary. Auto ARIMA model is applied on New Delhi's dewpoint data as the data is non-stationary. The implementation uses Forward Fill method in the data cleaning process to fill missing values. We proposed a modification in the data cleaning process- to fill missing values using the mean of the observations. ARIMA and Auto ARIMA are then applied on the modified temperature and dewpoint data. Mean Squared Error(MSE) is used to evaluate the model's performance for a certain data cleaning method.

We observe that for Stationary data, Forward fill Data Cleaning in ARIMA forecasting is a better approach than Mean of the Observations to fill missing values. For Non-stationary data, Mean of the Observations Data Cleaning process in Auto ARIMA forecasting is a better approach than Forward fill to fill missing values.

List of files added:

Dataset

• testset.csv (Delhi's weather data from 2000-2017)
• delhi_temperature.csv (Delhi's temperature data from testset.csv)
• delhi_dewpoint.csv (Delhi's dewpoint data from testset.csv)

Python Codes

• temperature_arima_forecasting.ipynb (ARIMA model for temperature forecasting)
• dewpoint_arima_forecasting.ipynb (Auto ARIMA model for dewpoint forecasting)
• temperature_arima_forecasting_modified.ipynb (ARIMA model for temperature forecasting with Data Preprocessing modification)
• dewpoint_arima_forecasting_modified.ipynb (Auto ARIMA model for dewpoint forecasting with Data Preprocessing modification)