Jupyter Notebooks Collection for Learning Time Series Models

My Kaggle Projects

Notebooks, projects and study material of the 'Sequences, Time Series and Prediction' course by deeplearning.ai

Notebooks de estudo e trabalho tech challenge da fase 02 do curso de data analytics

Gold-Price-forecasting In a personal endevaour to learn about time series analysis and forecasting, I decided to reserach and explore various quantitative forecasting methods.This notebook documents contains the methods that can be applied to forecast gold price and model deployment using streamlit, along with a detailed explaination of the diff…

📊 Sales Forecasting Optimisation using ARIMA, Prophet, and LSTM in Python — built with Google Colab & Anaconda Jupyter Notebook for demand planning and business insights.

In this notebook, I've loaded historical Dollar-Yen exchange rate futures data. I've applied time series analysis and modeling to determine whether there is any predictable behavior.

Code of Competencies This repository contains codes and notebooks with exploration data of the different platform competitions, such as: Kaggle, Tianchi, etc. In addition, I put more codes with modification in my original codes.

This repository provides a comprehensive exploration of Support Vector Machine (SVM) techniques applied to time series data, with a focus on financial FOREX price forecasting. It includes detailed code and examples, such as the 'Support Vector Machine Model.ipynb' notebook, which demonstrates preprocessing, model development and evaluation.

Wind Power Generation Forecasting using Machine Learning A data-driven forecasting project that uses machine learning models to predict wind power generation based on historical energy and weather data. Includes preprocessing, EDA, model training, and performance evaluation—all inside a well-documented Jupyter Notebook.

This project leverages time-series analysis and machine learning techniques to forecast CO₂ emissions based on historical data. By employing models like ARIMA, Prophet, and regression approaches, it aims to provide accurate predictions and uncover key trends to inform sustainable decision-making. Developed entirely in Kaggle Notebooks.

This repository contains an introduction to the Time_series fundamentals in TensorFlow and also an end-to-end forecasting model that is going to predict the future price of Bitcoin, based on the historical price of Bitcoin. The model built in this notebook follows a supervised learning approach.