{
  "annotation_id": "37c16cb8-7bfa-4cb4-9baa-e99b3bf6fae1",
  "date_created": "2026-03-02T18:00:43.218000Z",
  "date_modified": "2026-03-02T18:00:43.218000Z",
  "file_hash": "4c51ad9d86d3f2a72005e96848f58a89eb0ebc3a1743d6a71ecb172e15b5e840",
  "private": false,
  "record": {
    "abstract": "In this paper the author proposes to use the Least Squares Lattice filter\nwith forgetting factor to estimate time-varying parameters of the model for\nnoise processes. We simulated an Auto-Regressive (AR) noise process in which we\nlet the parameters of the AR vary in time. We investigate a new way of\nimplementation of Least Squares Lattice filter in following the non stationary\ntime series for stochastic process. Moreover we introduce a modified Least\nSquares Lattice filter to whiten the time-series and to remove the non\nstationarity. We apply this algorithm to the identification of real times\nseries data produced by recorded voice.",
    "arxiv_id": "physics/0211077",
    "authors": [
      "Elena Cuoco"
    ],
    "categories": [
      "physics.data-an",
      "physics.ins-det"
    ],
    "title": "A modified Least Squares Lattice filter to identify non stationary   process",
    "url": "https://arxiv.org/abs/physics/0211077"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "94bc7022-1cc0-40b7-9946-872c354697d4",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}