{"adoption": {"forks": 286, "observed_at": "2026-08-28T04:03:30.669566+00:00", "stars": 1081}, "canonical_url": "https://ross.abutalabs.com/products/seq2seq-signal-prediction", "card": {"archived": false, "artifact_type": "learning-resource", "description": "Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier", "domain": ["machine-learning", "deep-learning", "tutorials", "time-series"], "enriched": true, "function": ["machine-learning", "deep-learning"], "health_score": 20, "homepage": null, "language": "Jupyter Notebook", "license": "Apache-2.0", "license_family": "permissive", "maturity": "maintenance", "member_repos": ["guillaume-chevalier/seq2seq-signal-prediction"], "name": "guillaume-chevalier/seq2seq-signal-prediction", "platform": ["python", "cross-platform"], "pushed_at": "2023-03-25T00:33:48+00:00", "repo": "guillaume-chevalier/seq2seq-signal-prediction", "stars": 1081, "tags": ["seq2seq", "rnn", "tensorflow", "time-series-forecasting", "jupyter-notebook", "encoder-decoder", "tutorial-exercises"], "topics": ["seq2seq", "tensorflow", "tensorflow-tutorials", "python"], "urls": [], "use_cases": ["learn how to code seq2seq encoder-decoder RNNs", "forecast time series signals with a neural network", "practice TensorFlow RNN exercises with increasing difficulty", "run a seq2seq forecasting notebook in Google Colab", "understand encoder-decoder architectures without attention", "adapt seq2seq models to other tasks like NLP"], "what_it_is": "A Jupyter notebook tutorial with four graded exercises for learning to build Encoder-Decoder Sequence-to-Sequence RNN models in TensorFlow for time series signal forecasting. It includes toy datasets, a Python script version, and Google Colab support.", "when_to_avoid": ["you need a production-ready forecasting library or maintained model code", "you want modern TensorFlow 2.x or PyTorch implementations", "you need attention-based transformers rather than basic seq2seq RNNs"], "when_to_choose": ["you want a hands-on, exercise-driven tutorial for seq2seq RNNs in TensorFlow", "you are learning time series forecasting with recurrent neural networks", "you prefer runnable notebooks with toy datasets and Colab GPU support"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/seq2seq-signal-prediction", "repo": "guillaume-chevalier/seq2seq-signal-prediction", "role": "main", "score": 32}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:03:30.669566+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T06:51:22.182639+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "eee2e1943cc063e53179a4e98f92b362b1092501f14ed830b08d3db709047fb2", "fetched_at": "2026-08-28T04:03:30.669566+00:00", "kind": "readme", "missing": false, "url": "https://github.com/guillaume-chevalier/seq2seq-signal-prediction"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:03:30.669566+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T06:51:22.182639+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "eee2e1943cc063e53179a4e98f92b362b1092501f14ed830b08d3db709047fb2", "fetched_at": "2026-08-28T04:03:30.669566+00:00", "kind": "readme", "missing": false, "url": "https://github.com/guillaume-chevalier/seq2seq-signal-prediction"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T06:51:22.182639+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "eee2e1943cc063e53179a4e98f92b362b1092501f14ed830b08d3db709047fb2", "fetched_at": "2026-08-28T04:03:30.669566+00:00", "kind": "readme", "missing": false, "url": "https://github.com/guillaume-chevalier/seq2seq-signal-prediction"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:03:30.669566+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:03:30.669566+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:03:30.669566+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T06:51:22.182639+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "eee2e1943cc063e53179a4e98f92b362b1092501f14ed830b08d3db709047fb2", "fetched_at": "2026-08-28T04:03:30.669566+00:00", "kind": "readme", "missing": false, "url": "https://github.com/guillaume-chevalier/seq2seq-signal-prediction"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:03:30.669566+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:03:30.669566+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T06:51:22.182639+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "eee2e1943cc063e53179a4e98f92b362b1092501f14ed830b08d3db709047fb2", "fetched_at": "2026-08-28T04:03:30.669566+00:00", "kind": "readme", "missing": false, "url": "https://github.com/guillaume-chevalier/seq2seq-signal-prediction"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:03:30.669566+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:03:30.669566+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:03:30.669566+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T06:51:22.182639+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "eee2e1943cc063e53179a4e98f92b362b1092501f14ed830b08d3db709047fb2", "fetched_at": "2026-08-28T04:03:30.669566+00:00", "kind": "readme", "missing": false, "url": "https://github.com/guillaume-chevalier/seq2seq-signal-prediction"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:03:30.669566+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:03:30.669566+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T06:51:22.182639+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "eee2e1943cc063e53179a4e98f92b362b1092501f14ed830b08d3db709047fb2", "fetched_at": "2026-08-28T04:03:30.669566+00:00", "kind": "readme", "missing": false, "url": "https://github.com/guillaume-chevalier/seq2seq-signal-prediction"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T06:51:22.182639+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "eee2e1943cc063e53179a4e98f92b362b1092501f14ed830b08d3db709047fb2", "fetched_at": "2026-08-28T04:03:30.669566+00:00", "kind": "readme", "missing": false, "url": "https://github.com/guillaume-chevalier/seq2seq-signal-prediction"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T06:51:22.182639+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "eee2e1943cc063e53179a4e98f92b362b1092501f14ed830b08d3db709047fb2", "fetched_at": "2026-08-28T04:03:30.669566+00:00", "kind": "readme", "missing": false, "url": "https://github.com/guillaume-chevalier/seq2seq-signal-prediction"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T06:51:22.182639+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "eee2e1943cc063e53179a4e98f92b362b1092501f14ed830b08d3db709047fb2", "fetched_at": "2026-08-28T04:03:30.669566+00:00", "kind": "readme", "missing": false, "url": "https://github.com/guillaume-chevalier/seq2seq-signal-prediction"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": ["no_releases"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 3442, "days_push": 1258, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 32, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}