yashdeep01/time-series-forecasting

Forecasting sea surface temperatures of Pacific Ocean using ARIMA model in Python.

Forecasting Sea Surface Temperature with ✨ ARIMA ✨

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

🤔 What is it?

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!

🚀 Open with Streamlit

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

💻 Run with Jupyter Notebook

Create conda environment 🐍

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

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

Clone this repo

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

Run 🛠️

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.

Data 💾

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.

Info 📔

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.