The Danger of Overfitting
Time series data is notoriously noisy. Deep learning models, given their massive parameter counts, have a strong tendency to memorize noise rather than learning true underlying signals, leading to catastrophic generalization failure.
Required Baselines
Never deploy a neural network without first tracking metrics against:
- Naive Drift: Predicting the last observed value.
- Seasonal Naive: Predicting the value from the previous season (e.g., last Monday).
- ARIMA / ETS: Classical statistical methods.
Feature Engineering over Architecture
Instead of building a complex LSTM, extract lag features, rolling means, and datetime components (day of week, month) and feed them into LightGBM. This approach is faster to train, easier to interpret, and usually more accurate.
Internal Resources
- Inference Cost Calculator
- Latency Estimator
- Tabular vs Deep Learning
- Local LLMs vs Managed APIs
- Time Series Baselines
- Tabular Model Selector
- Vision Architecture Selector
- VRAM Calculator
- Choosing Embeddings
- CNN vs ViT in 2024
- RLHF vs DPO
- RAG Chunking Strategies
- RAG Chunk Size Calculator
- Token Ratio Estimator
- Synthetic Data Generation
- Quantization Methods Explained
- Quantization Memory Savings
- Fine-Tuning vs LoRA
- LoRA Rank Calculator
- Multimodal Model Landscape