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I will build time series forecasting models in python
About this Gig
Transform historical data into highly accurate predictions. Whether forecasting future trends or detecting system anomalies before failures occur, I build robust, production-ready machine learning models designed for time-series and sequential data.
With my background in aerospace engineering and AI, I develop end-to-end predictive frameworks. This ranges from baseline statistical forecasting to advanced deep learning architectures used for estimating the Remaining Useful Life (RUL) of complex industrial equipment.
Technical Expertise Provided:
- Time-Series Forecasting: Trend prediction and multivariate analysis.
- Predictive Maintenance: Anomaly detection, condition monitoring, and fault prediction frameworks.
- Deep Learning Architectures: Custom neural networks designed for complex, non-linear sequential data patterns.
- Machine Learning: Model optimization using PyTorch, TensorFlow, Keras, Scikit-learn, and XGBoost.
I apply rigorous data preprocessing, advanced feature engineering, and hyperparameter tuning to ensure models are mathematically sound, highly accurate, and resistant to overfitting.
Please message me with a dataset sample and your project goals before placing an order!
Programming language:
Python
•
R
•
MATLAB
•
SQL
•
MLflow
Frameworks:
Scikit-learn
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Keras
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PyTorch
•
Panda
APIs:
Other
Tools:
Jupyter Notebook
•
OpenCV
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TensorFlow
•
Excel
•
MLflow
•
Colab
My Portfolio
FAQ
Q1: What kind of time-series data do you handle?
A1: I handle everything from standard business metrics (sales, inventory forecasting) to highly complex, multivariate industrial sensor data.
Q2: My data has gaps and missing timestamps. Is that a problem?
A2: Not at all. Time-series data is rarely perfect. I will handle resampling, interpolation, and missing value imputation during the data preprocessing phase to ensure the model trains correctly.
Q3: What is the difference between the Basic and Standard packages?
A3: The Basic package uses traditional forecasting models which are fast and great for straightforward data. The Standard package utilizes Deep Learning (like LSTMs or Neural Networks), which are required for highly complex, non-linear data patterns.
Q4: Do you build user interfaces?
A4: Yes! In the Premium package, I can deploy your forecasting model into an interactive Streamlit dashboard so you can visualize predictions without looking at any code.
