https://github.com/nour-zayed/time-series-forcasting-ml-dl-
End-to-end time series forecasting using both Machine Learning and Deep Learning models. Includes data preprocessing, EDA, feature scaling, and performance evaluation on real-world datasets.
https://github.com/nour-zayed/time-series-forcasting-ml-dl-
deep-learning forecasting-models lstm machine-learning minmaxscaler prophet prophet-facebook sequential-model time-series-forecasting
Last synced: 3 months ago
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End-to-end time series forecasting using both Machine Learning and Deep Learning models. Includes data preprocessing, EDA, feature scaling, and performance evaluation on real-world datasets.
- Host: GitHub
- URL: https://github.com/nour-zayed/time-series-forcasting-ml-dl-
- Owner: Nour-Zayed
- Created: 2025-07-05T20:44:23.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-05T21:02:12.000Z (3 months ago)
- Last Synced: 2025-07-05T21:35:35.528Z (3 months ago)
- Topics: deep-learning, forecasting-models, lstm, machine-learning, minmaxscaler, prophet, prophet-facebook, sequential-model, time-series-forecasting
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## Time Series Forecasting — ML & DL End-to-End Project
### 📌 Overview:
A practical time series forecasting project applying both traditional Machine Learning techniques and advanced Deep Learning architectures.
The repository includes full data preprocessing, visualization, and prediction workflows on real-world time series datasets: avocado prices and vehicle miles traveled.## 📂 Project Structure:
### Time_Series_Forcasting(TSF)ML.ipynb
👉 Forecasting avocado prices using:Linear Regression
Random Forest Regressor
Prophet (Facebook’s time series model)
📌 Includes:
Data loading & sorting by date.
Data exploration (info(), describe()).
Handling time series ordering.
Applying and evaluating ML models.
Model comparison using MAE / MSE.
### Time_Series_Forcasting_(TSF)_DL.ipynb
👉 Forecasting vehicle miles traveled using:LSTM Neural Network (Keras Sequential model)
📌 Includes:
Data exploration & visualization (plot()).
Setting time-based index.
Feature scaling using MinMaxScaler.
Data reshaping for LSTM input.
Building and training an LSTM model.
Forecasting and plotting predictions.
Evaluating performance via MSE / MAE.
📊 Tools & Libraries:
PythonPandas, Numpy
Matplotlib, Seaborn
Scikit-learn
Keras (TensorFlow backend)
Prophet
## 📈 Key Highlights:
Real-world time series datasets.End-to-end workflow from data preprocessing to model training and prediction.
Clear visualizations to interpret trends and model performance.
Hybrid use of classical ML regressors and sequence-based DL models.
Hands-on implementation of Prophet and LSTM for time series.