Code repository for the online course "Feature Engineering for Time Series Forecasting".
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Updated
Dec 6, 2023 - Jupyter Notebook
Code repository for the online course "Feature Engineering for Time Series Forecasting".
Forecasting Time Series with Moving Average and Exponential Smoothing
Investigation of the capabilities of foundations models in the context of time series forecasting
Solar Irradiance Forecasting Using Deep Learning Techniques
Analysis Sales data to gain insights and create Interactive Sales Dashboard and also predict /Forecast the next sales with the use of Power-Bi.
Official repo for the following paper: Traffic Forecasting on New Roads Unseen in the Training Data Using Spatial Contrastive Pre-Training (SCPT) (ECML PKDD DAMI '23)
Dhaka weather forecasting model trained using API from Open-Meteo.com
Exploring forecasting for energy consumption
In this section, we will use machine learning algorithms to perform time series analysis.
data and code associated with the publication "Age structure augments the predictive power of time series for fisheries and conservation" by Tara E. Dolan, Eric P. Palkovacs, Tanya L. Rogers, and Stephan B. Munch.
💲 🔮 Predict 3 months of item-level sales data at different store locations and optimize budgets and placements to maximize key product metrics
A project from dicoding Machine Learning Intermediate Class with data Time Series
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.
The day-ahead prediction of electricity production from a run-of-river hydropower plant.
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.
A multivariate time series forecasting of pollution data using ARIMA, LM & ARIMAX in R
Highly accurate forecasting model for predicting Bitcoin prices in January 2024 using deep learning techniques, specifically Recurrent Neural Networks (RNNs).
Analyzing retail sales data to craft targeted marketing, elevate customer experiences, and forecast future sales.
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