Time-Series Predictive AI Model

$30-250 USD

Closed

Posted 11 days ago

$30-250 USD

Paid on delivery

I’m looking for an expert who can build a reliable predictive AI model that works with time-series data. I have the raw datasets ready; what I need is the full modeling workflow—from exploratory analysis and feature engineering through training, validation, and clear performance reporting. Key objectives: • Choose the most suitable algorithms (e.g., ARIMA, Prophet, LSTM, or hybrids) and justify the selection. • Handle data preprocessing, missing-value treatment, and scaling so the model remains robust in production. • Deliver reproducible code (Python preferred, using frameworks such as TensorFlow, PyTorch, or scikit-learn) and concise documentation that explains setup, hyperparameters, and retraining steps. • Provide evaluation metrics like MAE, RMSE, and visual forecasts to make results easy to interpret. If you have experience deploying models, please mention it, as a follow-up phase may include packaging the solution behind an API. Let me know your relevant projects with time-series forecasting so I can gauge fit quickly.

Machine Learning (ML)

Software Architecture

Big Data Sales

Project ID: 40424199

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4 proposals

Remote project

Active 11 days ago

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4 freelancers are bidding on average $119 USD for this job

@rbtech1984

Hello There!!! ★★★★ ( building time-series predictive AI model with full ML pipeline from EDA to forecasting & evaluation ) ★★★★ Project understanding: You need a complete time-series forecasting AI solution using your dataset, including preprocessing, feature engineering, model selection (ARIMA/Prophet/LSTM/hybrids), training, evaluation, and clear reporting with reproducible Python code and documentation. ⚜ Data preprocessing, missing value handling & scaling pipeline ⚜ Exploratory data analysis (EDA) with insights for time-series patterns ⚜ Model selection (ARIMA, Prophet, LSTM or hybrid approach with justification) ⚜ Feature engineering for seasonality, trends and lag variables ⚜ Training, validation & hyperparameter tuning using ML frameworks ⚜ Performance evaluation (MAE, RMSE, visual forecast plots) ⚜ Clean reproducible Python code + deployment-ready structure I have worked on ML forecasting models and predictive analytics pipelines using Python (scikit-learn, TensorFlow, statsmodels). I focus on building stable, explainable models rather than just accuracy, so results are production ready and easy to extend later into API deployment if needed. I can structure the workflow clearly so your team can retrain or scale it later without confusion. Let’s discuss dataset details and I can suggest the best model strategy quickly. Warm Regards, Farhin B.

$100 USD in 7 days

4.4

(3 reviews)

4.0

@AndrewFrolov

hi, i can help build a clean first forecasting workflow for your time-series dataset. i’d start practical: inspect the data first, check frequency, missing values, seasonality, outliers, and whether it is univariate or multivariate. after that i’d build a baseline model and compare a few suitable approaches instead of jumping straight to LSTM. my first phase would include: - EDA and data quality check - preprocessing / missing value handling - feature engineering for dates, lags, rolling stats if useful - model comparison, for example ARIMA/Prophet and a machine learning baseline - MAE/RMSE reporting - forecast plots - reproducible Python notebook/script - short setup and retraining notes if the results are good, the API packaging can be a second phase. quick question: what kind of time-series is this, sales, traffic, finance, sensor data, or something else? and is it one target series or multiple variables?

$180 USD in 4 days