Predict stock prices using neural networks trained on historical price data.
Forecasting footsteps in Walmart from previous years available timeseries data and predict on new years data.
Final Data Science group project for Henry Bootcamp. Developed data solutions for Olist, an ecommerce brazilian startup, including a Dashboard in PowerBI, a forecasting model deployed on Streamlit and a web app for remote access.
💲 🔮 Predict 3 months of item-level sales data at different store locations and optimize budgets and placements to maximize key product metrics
Random Forest algorithm to forecast the hourly power generation of PV plant over time, using time series data and multiple features.
replication code and data for missing data, speculative reading article
Predicting future stock prices based on past close prices and sentiment provided by a series of tweets. A time series forecasting survey using sentiment analysis.
A project from dicoding Machine Learning Intermediate Class with data Time Series
This Kaggle competition challenges participants to use a modified version of the POWER NASA Temperature Dataset to build forecasting models.
Forecasting the Energy Load Consumption using time series data
Two projects developed as part of a university course on Artificial Neural networks and Deep Learning, in particular, an image classification task, and a univariate time series forecasting task.
Forecasting EEG signals using Lag-Llama model
The "Cincinnati Traffic Crashes - Time Series Analysis" is a comprehensive study that employs statistical techniques to examine patterns and trends in traffic accidents over time within the Cincinnati area. This analysis aims to forecast future incidents, and assist in developing strategies to enhance road safety.
Analyze and propose the plan to monitor and estimate business aspects
The day-ahead prediction of electricity production from a run-of-river hydropower plant.
Exploring forecasting for energy consumption
Made a R Markdown report by data cleaning, data visualizing, data analyzing and forecasting using R of monthly data of Unemployment rate of Canada aged of 15 and more in last 40 years. By fitting various models (Naïve, ETS, STL based models) and checking their accuracy, I forecasted best possible results.
In this section, we will use machine learning algorithms to perform time series analysis.
Long-term solar activity forecast for solar cycles 25 and 26 with libraries numpy, pandas, scipy, sympy, sklearn. A science project by physics undergrad student.
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