| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - futurepredictionai |
| - ai |
| pipeline_tag: time-series-forecasting |
| --- |
| # Future Prediction Models (6 topics + Unified) |
|
|
| An end-to-end multi-domain AI forecasting system. Real datasets, two model modes, ChatGPT-style predictions. |
|
|
| ## Models: separate vs unified |
|
|
| - **Separate** (`model_<topic>.pt`) — 6 dedicated 2-layer LSTMs, one per topic |
| - **Unified** (`unified_model.pt`) — One combined model for all 6 domains: shared LSTM backbone + per-domain embedding + per-domain heads, trained jointly from merged separate models |
|
|
| The unified model merges weights from all 6 trained separate models into a single checkpoint, enabling cross-domain knowledge transfer and a single model that can predict any of the 6 topics. |
|
|
| ## Topics & datasets (all real, fetched automatically) |
|
|
| | Topic | Asset | Source | Points | |
| |---|---|---|---| |
| | **AI** | NVIDIA daily close | Yahoo Finance | 6,938 | |
| | **Programming** | Daily `react` npm downloads | npm registry API | 547 | |
| | **Finance** | Bitcoin BTC-USD close | Yahoo Finance (Hugging Face fallback) | 4,252 | |
| | **Sports** | ATP world #1 Elo rating | Hugging Face tennis (93,028 matches, Elo computed from results) | 10,944 | |
| | **Weather** | Daily mean temperature (any city) | Open-Meteo archive | 4,248 | |
| | **Economy** | S&P 500 daily close | Yahoo Finance | 6,699 | |
|
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| Weather city is configurable. Every topic falls back gracefully: Hugging Face search -> API -> synthetic data if fully offline. |
|
|
| ## Validated accuracy (held-out test set, no data leakage) |
|
|
| **Separate per-topic models:** |
|
|
| | Topic | MAE | H1 MAPE | vs naive baseline | |
| |---|---|---|---| |
| | AI (NVIDIA) | $2.17 | 2.26% | ~naive | |
| | Economy (S&P 500) | $37.37 | 0.71% | beats naive by 0.5% | |
| | Finance (Bitcoin) | $1,437.46 | 1.61% | ~naive | |
| | Programming (react) | 1.17M downloads | 7.12% | beats naive by 77.5% | |
| | Sports (ATP #1 Elo) | 3.40 Elo | 0.06% | ~naive | |
| | Weather (Chennai temp) | 0.99 K | 0.18% | beats naive by 1.9% | |
|
|
| **Unified single model (merged from 6 separate models):** |
|
|
| | Topic | MAE | H1 MAPE | |
| |---|---|---| |
| | AI | $3.40 | 2.36% | |
| | Economy | $72.21 | 0.75% | |
| | Finance | $2,863.45 | 1.74% | |
| | Programming | 1.14M downloads | 5.42% | |
| | Sports | 2.75 Elo | 0.04% | |
| | Weather | 0.94 K | 0.18% | |
|
|
| > Markets behave near a random walk, so 100% accuracy is impossible — these are honest, validated numbers. No model can guarantee the future. |
|
|
| ## Architecture (normal mode shows only the final response) |
|
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| User question -> intent detection -> prediction engine -> response formatter -> chat output |
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| - Chat CLI — only the final assistant response reaches the user |
| - Intent detection + orchestration (topic, location, horizon, style) |
| - Prediction engine — runs the LSTM, returns structured metrics: net change, forecast range, current-to-forecast, direction, primary forecast |
| - Response formatter — presents metrics naturally; opens with a one-sentence ChatGPT-style summary; never invents confidence, probability, or values |
| - Multi-topic data pipeline (fetchers + caching) |
| - 2-layer LSTM, Huber loss, AdamW, early stopping, temporal split |
| - Per-topic model loader, rollout forecast, chart + text report |
|
|
| ## Ask it anything (ChatGPT-style) |
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| Interactive chat with natural language queries: |
| - "what will bitcoin do next week?" |
| - "compare all topics" |
| - "predict programming for 14 days" |
| - "predict ai for 3 days" |
|
|
| Example response: |
| ``` |
| In short, the model expects Bitcoin to stay roughly stable next week, hovering near $63,228. |
| |
| Bitcoin Forecast |
| August 18-24, 2026 |
| |
| The model forecasts relatively sideways movement for Bitcoin next week, with an |
| estimated level around **$63,228**. |
| |
| Outlook: Sideways |
| Predicted level (2026-08-24): ~$63,228 |
| Expected change: <0.01% |
| Forecast range: <0.01% |
| From latest observed value ($63,229, 2026-08-17): <0.01% |
| |
| This is a model-generated forecast, and actual market behavior may differ. |
| ``` |
|
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| ## Weather for any city |
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| Configure city via environment variables or city registry, then query weather predictions. |
|
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| ## Train / refresh |
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| Run training for all topics or individual domains. Outputs: model checkpoints, configs, predictions, charts, and results. |
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| ## Add a new domain |
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| 1. Add entry to topics configuration (label, unit, asset name) |
| 2. Add a fetcher returning date + value columns |
| 3. Run training — everything else is automatic |
|
|
| ## Hugging Face Repository |
|
|
| All models and configs are available at: |
| **https://huggingface.co/CodeDevX/future-prediction-multi-domain-lstm** |
