A small interactive web-app to visualise forecasting as you slide the timeframe ahead!

Implementation of ARIMA model to forecast sea surface temperatures at equitorial Pacific. All the heavy lifting of time-series data cleaning and training the model is already done for! Just hop on to the web-app and view inference live!

Visualise the forecast on your browser. Tune the timeframe window as you watch your model forecast.

Install the anaconda package from here and run these commands on terminal:

conda init
conda create -n forecast python=3.8
conda activate forecast

git clone https://github.com/yashdeep01/Time-Series-Forecasting.git
cd Time-Series-Forecasting/
pip install -r requirements.txt

jupyter notebook

Your default browser must open up with Jupyter home page at localhost:8888/tree. Select time-series.ipynb in files and notebook opens in a new tab.

Kaggle dataset: https://www.kaggle.com/uciml/el-nino-dataset

Dataset used here contains surface sea temperature readings taken daily from a series of buoys positioned at the equatorial Pacific. All readings were taken at the same time of the day. This data is used to understand and predict seasonal-to-inter annual climate variations originating in the tropics. Time series data used for training covers a span of 4 years — from 1 January, 1993 to 31 December, 1996. There are missing values in the data which are treated by linear interpolation here.

Find R script in this repo at ./script/arima_forecasting.R. Also find the implementation details (data deep dive, testing, modelling parameters) and theory at ./docs/Forecasting with ARIMA.pdf.