You will get I will build a Python time series forecasting prototype
Project details
I build practical Python time-series forecasting prototypes for clients who need a clear, reproducible forecasting workflow and decision-ready outputs. This project is a good fit if you already have time-series data and want a working prototype with prediction plots, basic evaluation metrics, and clean source code.
Depending on the package, I can also include benchmark comparison, model validation, and a short results summary. My goal is to give you a forecasting workflow that is understandable, usable, and easy to extend. This service is best for early-stage forecasting tasks, model comparison, and prototype development rather than large-scale production systems.
Depending on the package, I can also include benchmark comparison, model validation, and a short results summary. My goal is to give you a forecasting workflow that is understandable, usable, and easy to extend. This service is best for early-stage forecasting tasks, model comparison, and prototype development rather than large-scale production systems.
Machine Learning Tools
Keras, Microsoft Excel, NumPy, pandas, Python, PyTorch, scikit-learn, SciPy, SQL, TensorFlowWhat's included
| Service Tiers |
Starter
$129
|
Standard
$229
|
Advanced
$599
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 2 |
Number of Model Variations | 1 | 2 | 3 |
Number of Graphs/Charts | 2 | 4 | 6 |
Model Validation/Testing | - | ||
Model Documentation | - | - | |
Data Source Connectivity | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$39
Additional Model Variation
(+ 2 Days)
+$89
Additional Graph/Chart
(+ 1 Day)
+$29
Model Validation/Testing
(+ 2 Days)
+$99About James
Time-Series Forecasting & Applied ML Engineer | Python | Model Deploym
Beijing, China - 8:45 am local time
My work focuses on Python-based modeling, data preprocessing, model evaluation, backtesting, uncertainty visualization, and lightweight deployment workflows. I have experience with TensorFlow, PyTorch, scikit-learn, pandas, NumPy, Matplotlib, Jupyter, Gradio, Hugging Face Spaces, GitHub Actions, and FastAPI-style prototype delivery.
A recent portfolio example is a deployed Mekong water-level forecasting system with live 7-day prediction, backtesting, persistence baseline comparison, upstream-assisted correction, uncertainty visualization, GitHub Actions validation, and a production-oriented AWS ECS/Fargate backend case study.
I can help with:
• Time-series forecasting prototypes
• Data cleaning and feature preparation
• Model evaluation and backtesting
• Baseline comparison and error analysis
• Gradio / Hugging Face demo apps
• FastAPI-style ML inference prototypes
• Reproducible GitHub project cleanup, README, tests, and CI
If your project involves messy real-world data, forecasting, model evaluation, or turning an ML prototype into something usable, I can help build a clear, practical, and well-documented solution.
Steps for completing your project
After purchasing the project, send requirements so James can start the project.
Delivery time starts when James receives requirements from you.
James works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1
Review the dataset and define the forecasting target I review the dataset, confirm the target variable, data frequency, and forecast horizon, and identify any major data issues before modeling.
Step 2
Prepare the data and set up the baseline workflow I clean and organize the data, prepare the forecasting inputs, and build a baseline workflow for comparison if needed.