Hybrid CNN+LSTM Sales Forecasting
When classical time-series models plateau, hybrid deep learning can capture complex demand patterns.
Problem & industry context
Subscription and QSR businesses forecast demand for inventory, staffing, and promotions. ARIMA and SARIMAX excel on smooth series; sudden campaigns, weather, and holidays introduce non-linear spikes. Teams often ensemble statistical baselines with neural models and validate on hold-out weeks, not just RMSE on training data.
Insight
CNN layers extract local temporal patterns; LSTM layers model longer dependencies. Comparing Random Forest, ARIMA, SARIMAX, and hybrid nets on the same splits avoids "winner by default." Production forecasting needs reproducible pipelines (SageMaker, versioned features) and stakeholder-readable error bands—not a single leaderboard number.
What I built
Implemented a sales forecasting system achieving 93.17% accuracy using a CNN+LSTM hybrid, benchmarked against Random Forest, ARIMA, and SARIMAX. Packaged training and inference for AWS SageMaker deployment alongside the meal subscription product stack.
Technical approach
Stack and tooling for this work: Python, CNN, LSTM, Random Forest, ARIMA, SARIMAX, AWS SageMaker. Topics covered: Time Series, CNN, LSTM, MLOps.
Topics
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