diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 0225aa6..a5ad52f 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -23,7 +23,7 @@ jobs:
       # e.g. poetry version 0.1.${{ github.run_number }}
       - name: Set Version number
         run: |
-          poetry version 1.2.1
+          poetry version 1.2.2
       - name: Build and Publish to PyPI
         run: |
           poetry config pypi-token.pypi ${{ secrets.PYPI_API_TOKEN }}
diff --git a/notebooks/finetuning.ipynb b/notebooks/finetuning.ipynb
index 31da8f3..c29b52d 100644
--- a/notebooks/finetuning.ipynb
+++ b/notebooks/finetuning.ipynb
@@ -62,15 +62,14 @@
    "outputs": [],
    "source": [
     "tfm = timesfm.TimesFm(\n",
-    "    context_len=512,\n",
-    "    horizon_len=128,\n",
-    "    input_patch_len=32,\n",
-    "    output_patch_len=128,\n",
-    "    num_layers=20,\n",
-    "    model_dims=1280,\n",
-    "    backend=\"gpu\",\n",
-    ")\n",
-    "tfm.load_from_checkpoint(repo_id=\"google/timesfm-1.0-200m\")"
+    "      hparams=timesfm.TimesFmHparams(\n",
+    "          backend=\"gpu\",\n",
+    "          per_core_batch_size=32,\n",
+    "          horizon_len=128,\n",
+    "      ),\n",
+    "      checkpoint=timesfm.TimesFmCheckpoint(\n",
+    "          huggingface_repo_id=\"google/timesfm-1.0-200m\"),\n",
+    "  )"
    ]
   },
   {
@@ -209,8 +208,8 @@
     "for batch in tqdm(test_batches.as_numpy_iterator()):\n",
     "    past = batch[0]\n",
     "    actuals = batch[3]\n",
-    "    _, forecasts = tfm.forecast(list(past), [0] * past.shape[0])\n",
-    "    forecasts = forecasts[:, 0 : actuals.shape[1], 5]\n",
+    "    forecasts, _ = tfm.forecast(list(past), [0] * past.shape[0], normalize=True)\n",
+    "    forecasts = forecasts[:, 0 : actuals.shape[1]]\n",
     "    mae_losses.append(np.abs(forecasts - actuals).mean())\n",
     "\n",
     "print(f\"MAE: {np.mean(mae_losses)}\")"
diff --git a/poetry.lock b/poetry.lock
index 297aa01..b359f12 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -247,21 +247,20 @@ lxml = ["lxml"]
 
 [[package]]
 name = "bleach"
-version = "6.1.0"
+version = "6.2.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"},
-    {file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"},
+    {file = "bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e"},
+    {file = "bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"},
 ]
 
 [package.dependencies]
-six = ">=1.9.0"
 webencodings = "*"
 
 [package.extras]
-css = ["tinycss2 (>=1.1.0,<1.3)"]
+css = ["tinycss2 (>=1.1.0,<1.5)"]
 
 [[package]]
 name = "cachetools"
@@ -1162,13 +1161,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2024.9.0"
+version = "2024.10.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2024.9.0-py3-none-any.whl", hash = "sha256:a0947d552d8a6efa72cc2c730b12c41d043509156966cca4fb157b0f2a0c574b"},
-    {file = "fsspec-2024.9.0.tar.gz", hash = "sha256:4b0afb90c2f21832df142f292649035d80b421f60a9e1c027802e5a0da2b04e8"},
+    {file = "fsspec-2024.10.0-py3-none-any.whl", hash = "sha256:03b9a6785766a4de40368b88906366755e2819e758b83705c88cd7cb5fe81871"},
+    {file = "fsspec-2024.10.0.tar.gz", hash = "sha256:eda2d8a4116d4f2429db8550f2457da57279247dd930bb12f821b58391359493"},
 ]
 
 [package.extras]
@@ -1374,70 +1373,70 @@ test = ["coverage", "mock (>=4)", "pytest (>=7)", "pytest-cov", "pytest-mock (>=
 
 [[package]]
 name = "grpcio"
-version = "1.67.0"
+version = "1.67.1"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.8"
 files = [
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@@ -1536,13 +1535,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
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+    {file = "tokenizers-0.20.3-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:de082392a85eb0055cc055c535bff2f0cc15d7a000bdc36fbf601a0f3cf8507a"},
+    {file = "tokenizers-0.20.3-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:c3db46cc0647bfd88263afdb739b92017a02a87ee30945cb3e86c7e25c7c9917"},
+    {file = "tokenizers-0.20.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a292392f24ab9abac5cfa8197e5a6208f2e43723420217e1ceba0b4ec77816ac"},
+    {file = "tokenizers-0.20.3-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8dcd91f4e60f62b20d83a87a84fe062035a1e3ff49a8c2bbdeb2d441c8e311f4"},
+    {file = "tokenizers-0.20.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:900991a2b8ee35961b1095db7e265342e0e42a84c1a594823d5ee9f8fb791958"},
+    {file = "tokenizers-0.20.3-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:5a8d8261ca2133d4f98aa9627c748189502b3787537ba3d7e2beb4f7cfc5d627"},
+    {file = "tokenizers-0.20.3-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:c4fd4d71e6deb6ddf99d8d0eab87d1d16f635898906e631914a9bae8ae9f2cfb"},
+    {file = "tokenizers-0.20.3.tar.gz", hash = "sha256:2278b34c5d0dd78e087e1ca7f9b1dcbf129d80211afa645f214bd6e051037539"},
 ]
 
 [package.dependencies]
@@ -6069,13 +6082,13 @@ files = [
 
 [[package]]
 name = "tqdm"
-version = "4.66.5"
+version = "4.66.6"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.5-py3-none-any.whl", hash = "sha256:90279a3770753eafc9194a0364852159802111925aa30eb3f9d85b0e805ac7cd"},
-    {file = "tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad"},
+    {file = "tqdm-4.66.6-py3-none-any.whl", hash = "sha256:223e8b5359c2efc4b30555531f09e9f2f3589bcd7fdd389271191031b49b7a63"},
+    {file = "tqdm-4.66.6.tar.gz", hash = "sha256:4bdd694238bef1485ce839d67967ab50af8f9272aab687c0d7702a01da0be090"},
 ]
 
 [package.dependencies]
@@ -6104,13 +6117,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "transformers"
-version = "4.45.2"
+version = "4.46.2"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "transformers-4.45.2-py3-none-any.whl", hash = "sha256:c551b33660cfc815bae1f9f097ecfd1e65be623f13c6ee0dda372bd881460210"},
-    {file = "transformers-4.45.2.tar.gz", hash = "sha256:72bc390f6b203892561f05f86bbfaa0e234aab8e927a83e62b9d92ea7e3ae101"},
+    {file = "transformers-4.46.2-py3-none-any.whl", hash = "sha256:c921f4406b78e6518c97b618c5acd1cf8a4f2315b6b727f4bf9e01496eef849c"},
+    {file = "transformers-4.46.2.tar.gz", hash = "sha256:3d85410881e1c074be767877bf33c83231ec11529f274a6044ecb20c157ba14e"},
 ]
 
 [package.dependencies]
@@ -6128,13 +6141,13 @@ tqdm = ">=4.27"
 [package.extras]
 accelerate = ["accelerate (>=0.26.0)"]
 agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
-all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision"]
+all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision"]
 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 benchmark = ["optimum-benchmark (>=0.3.0)"]
 codecarbon = ["codecarbon (==1.2.0)"]
 deepspeed = ["accelerate (>=0.26.0)", "deepspeed (>=0.9.3)"]
 deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
 dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.20,<0.21)", "urllib3 (<2.0.0)"]
 dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "libcst", "librosa", "nltk (<=3.8.1)", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
@@ -6168,7 +6181,7 @@ torch = ["accelerate (>=0.26.0)", "torch"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
 torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.20,<0.21)", "torch", "tqdm (>=4.27)"]
-video = ["av (==9.2.0)", "decord (==0.6.0)"]
+video = ["av (==9.2.0)"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
@@ -6269,13 +6282,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "utilsforecast"
-version = "0.2.5"
+version = "0.2.7"
 description = "Forecasting utilities"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "utilsforecast-0.2.5-py3-none-any.whl", hash = "sha256:ad41b2e387eaade62e5505086c8a6fd2145805a757079d9796718273950eea4a"},
-    {file = "utilsforecast-0.2.5.tar.gz", hash = "sha256:b69486e17454170cb180fe8a7d19340ac06032c3b03e2119d417ba4d830246d9"},
+    {file = "utilsforecast-0.2.7-py3-none-any.whl", hash = "sha256:ee09f03d018e1cdccd382c91897396849c5f1330361579eb7b3cfe3c6cc84e91"},
+    {file = "utilsforecast-0.2.7.tar.gz", hash = "sha256:968b430a7f966dd520dc98dbac3530d2b4a2fa3f98319ece8bc55c431378abbb"},
 ]
 
 [package.dependencies]
@@ -6389,13 +6402,13 @@ test = ["websockets"]
 
 [[package]]
 name = "werkzeug"
-version = "3.0.4"
+version = "3.1.2"
 description = "The comprehensive WSGI web application library."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "werkzeug-3.0.4-py3-none-any.whl", hash = "sha256:02c9eb92b7d6c06f31a782811505d2157837cea66aaede3e217c7c27c039476c"},
-    {file = "werkzeug-3.0.4.tar.gz", hash = "sha256:34f2371506b250df4d4f84bfe7b0921e4762525762bbd936614909fe25cd7306"},
+    {file = "werkzeug-3.1.2-py3-none-any.whl", hash = "sha256:4f7d1a5de312c810a8a2c6f0b47e9f6a7cffb7c8322def35e4d4d9841ff85597"},
+    {file = "werkzeug-3.1.2.tar.gz", hash = "sha256:f471a4cd167233077e9d2a8190c3471c5bc520c636a9e3c1e9300c33bced03bc"},
 ]
 
 [package.dependencies]
diff --git a/src/timesfm/timesfm_base.py b/src/timesfm/timesfm_base.py
index 4d60a4c..230fb82 100644
--- a/src/timesfm/timesfm_base.py
+++ b/src/timesfm/timesfm_base.py
@@ -62,7 +62,7 @@ def freq_map(freq: str):
 
 
 # Per time series normalization: forward.
-def normalize(batch):
+def _normalize(batch):
   stats = [
       (np.mean(x), np.where((w := np.std(x)) > _TOL, w, 1.0)) for x in batch
   ]
@@ -71,7 +71,7 @@ def normalize(batch):
 
 
 # Per time series normalization: inverse.
-def renormalize(batch, stats):
+def _renormalize(batch, stats):
   return [x * stat[1] + stat[0] for x, stat in zip(batch, stats)]
 
 
@@ -109,6 +109,8 @@ class TimesFmHparams:
   per_core_batch_size: int = 32
   backend: Literal["cpu", "gpu", "tpu"] = "cpu"
   quantiles: Sequence[float] | None = DEFAULT_QUANTILES
+  # Hparams beyond the model.
+  point_forecast_mode: Literal["mean", "median"] = "median"
 
 
 @dataclasses.dataclass(kw_only=True)
@@ -184,8 +186,9 @@ def load_from_checkpoint(self, checkpoint: TimesFmCheckpoint) -> None:
     """Loads a checkpoint and compiles the decoder."""
     raise NotImplementedError("`load_from_checkpoint` is not implemented.")
 
-  def _preprocess(self, inputs: Sequence[np.array],
-                  freq: Sequence[int]) -> tuple[np.array, np.array, int]:
+  def _preprocess(
+      self, inputs: Sequence[np.ndarray],
+      freq: Sequence[int]) -> tuple[np.ndarray, np.ndarray, np.ndarray, int]:
     """Formats and pads raw inputs to feed into the model.
 
     This function both pads each time series to match the context length, and
@@ -200,6 +203,7 @@ def _preprocess(self, inputs: Sequence[np.array],
     A tuple of:
     - the padded input time series to meet the model required context.
     - the padding indicator.
+    - the frequency of each input time series.
     - the number of padded examples for SPMD so that each core has the same
         number (a multiple of `batch_size`) of examples.
     """
@@ -239,6 +243,39 @@ def _preprocess(self, inputs: Sequence[np.array],
         pmap_pad,
     )
 
+  def _forecast(
+      self,
+      inputs: Sequence[Any],
+      freq: Sequence[int] | None = None,
+      window_size: int | None = None,
+      forecast_context_len: int | None = None,
+      return_forecast_on_context: bool = False,
+  ) -> tuple[np.ndarray, np.ndarray]:
+    """Forecasts on a list of time series.
+
+    Args:
+      inputs: list of time series forecast contexts. Each context time series
+        should be in a format convertible to JTensor by `jnp.array`.
+      freq: frequency of each context time series. 0 for high frequency
+        (default), 1 for medium, and 2 for low. Notice this is different from
+        the `freq` required by `forecast_on_df`.
+      window_size: window size of trend + residual decomposition. If None then
+        we do not do decomposition.
+      forecast_context_len: optional max context length.
+      return_forecast_on_context: True to return the forecast on the context
+        when available, i.e. after the first input patch.
+
+    Returns:
+    A tuple for np.array:
+    - the mean forecast of size (# inputs, # forecast horizon),
+    - the full forecast (mean + quantiles) of size
+        (# inputs,  # forecast horizon, 1 + # quantiles).
+
+    Raises:
+    ValueError: If the checkpoint is not properly loaded.
+    """
+    raise NotImplementedError("`_forecast` is not implemented.")
+
   def forecast(
       self,
       inputs: Sequence[Any],
@@ -246,8 +283,8 @@ def forecast(
       window_size: int | None = None,
       forecast_context_len: int | None = None,
       return_forecast_on_context: bool = False,
-      truncate_negative: bool = False,
-  ) -> tuple[np.array, np.array]:
+      normalize: bool = False,
+  ) -> tuple[np.ndarray, np.ndarray]:
     """Forecasts on a list of time series.
 
     Args:
@@ -261,11 +298,11 @@ def forecast(
       forecast_context_len: optional max context length.
       return_forecast_on_context: True to return the forecast on the context
         when available, i.e. after the first input patch.
-      truncate_negative: truncate to only non-negative values if all the contexts
-        have non-negative values.
+      normalize: If True, then we normalize the inputs before forecasting and
+        the outputs are then renormalized to the original scale.
 
     Returns:
-    A tuple for JTensors:
+    A tuple for np.array:
     - the mean forecast of size (# inputs, # forecast horizon),
     - the full forecast (mean + quantiles) of size
         (# inputs,  # forecast horizon, 1 + # quantiles).
@@ -273,7 +310,42 @@ def forecast(
     Raises:
     ValueError: If the checkpoint is not properly loaded.
     """
-    raise NotImplementedError("`forecast` is not implemented.")
+    stats = None
+    if normalize:
+      inputs, stats = _normalize(inputs)
+    mean_forecast, quantile_forecast = self._forecast(
+        inputs,
+        freq,
+        window_size,
+        forecast_context_len,
+        return_forecast_on_context,
+    )
+    if stats is not None:
+      stats = np.array(stats)
+      mu = stats[:, 0]
+      sigma = stats[:, 1]
+      mean_forecast = mean_forecast * sigma[:, None] + mu[:, None]
+      quantile_forecast = (quantile_forecast * sigma[:, None, None] +
+                           mu[:, None, None])
+    if self.hparams.point_forecast_mode == "mean":
+      return mean_forecast, quantile_forecast
+    elif self.hparams.point_forecast_mode == "median":
+      if self._median_index == -1:
+        for i, quantile in enumerate(self.quantiles):
+          if quantile == 0.5:
+            self._median_index = i
+            break
+        if self._median_index == -1:
+          raise ValueError("Median (0.5) is not found in the model quantiles:"
+                           f" {self.quantiles}. Please check the hparams.")
+      return (
+          quantile_forecast[:, :, 1 + self._median_index],
+          quantile_forecast,
+      )
+    else:
+      raise ValueError(
+          "Unsupported point forecast mode:"
+          f" {self.hparams.point_forecast_mode}. Use 'mean' or 'median'.")
 
   def forecast_with_covariates(
       self,
@@ -408,7 +480,7 @@ def forecast_with_covariates(
       ]
       per_instance_stats = None
       if normalize_xreg_target_per_input:
-        targets, per_instance_stats = normalize(targets)
+        targets, per_instance_stats = _normalize(targets)
       xregs = xreg_lib.BatchedInContextXRegLinear(
           targets=targets,
           train_lens=train_lens,
@@ -431,7 +503,7 @@ def forecast_with_covariates(
           assert_covariate_shapes=True,
       )
       if normalize_xreg_target_per_input:
-        xregs = renormalize(xregs, per_instance_stats)
+        xregs = _renormalize(xregs, per_instance_stats)
       outputs = [
           (mean_output[self._horizon_start:(self._horizon_start + test_len)] +
            xreg)
@@ -446,7 +518,7 @@ def forecast_with_covariates(
       ]
       per_instance_stats = None
       if normalize_xreg_target_per_input:
-        targets, per_instance_stats = normalize(targets)
+        targets, per_instance_stats = _normalize(targets)
       xregs, xregs_on_context, _, _, _ = xreg_lib.BatchedInContextXRegLinear(
           targets=targets,
           train_lens=train_lens,
@@ -484,7 +556,7 @@ def forecast_with_covariates(
           for mean_output, test_len, xreg in zip(mean_outputs, test_lens, xregs)
       ]
       if normalize_xreg_target_per_input:
-        outputs = renormalize(outputs, per_instance_stats)
+        outputs = _renormalize(outputs, per_instance_stats)
 
     return outputs, xregs
 
diff --git a/src/timesfm/timesfm_jax.py b/src/timesfm/timesfm_jax.py
index 3d01d8f..adfbd55 100644
--- a/src/timesfm/timesfm_jax.py
+++ b/src/timesfm/timesfm_jax.py
@@ -234,14 +234,13 @@ def _decode(inputs):
           }))
     self._logging(f"Jitted decoding in {time.time() - start_time:.2f} seconds.")
 
-  def forecast(
+  def _forecast(
       self,
       inputs: Sequence[Any],
       freq: Sequence[int] | None = None,
       window_size: int | None = None,
       forecast_context_len: int | None = None,
       return_forecast_on_context: bool = False,
-      truncate_negative: bool = False,
   ) -> tuple[np.ndarray, np.ndarray]:
     """Forecasts on a list of time series.
 
@@ -256,8 +255,6 @@ def forecast(
       forecast_context_len: optional max context length.
       return_forecast_on_context: True to return the forecast on the context
         when available, i.e. after the first input patch.
-      truncate_negative: truncate to only non-negative values if all the contexts
-        have non-negative values.
 
     Returns:
     A tuple for JTensors:
@@ -277,7 +274,6 @@ def forecast(
     else:
       fcontext_len = forecast_context_len
     inputs = [np.array(ts)[-fcontext_len:] for ts in inputs]
-    inp_min = np.min([np.min(ts) for ts in inputs])
 
     if window_size is not None:
       new_inputs = []
@@ -352,7 +348,4 @@ def forecast(
     if window_size is not None:
       mean_outputs = mean_outputs[0::2, ...] + mean_outputs[1::2, ...]
       full_outputs = full_outputs[0::2, ...] + full_outputs[1::2, ...]
-    if inp_min >= 0 and truncate_negative:
-      mean_outputs = np.maximum(mean_outputs, 0.0)
-      full_outputs = np.maximum(full_outputs, 0.0)
     return mean_outputs, full_outputs
diff --git a/src/timesfm/timesfm_torch.py b/src/timesfm/timesfm_torch.py
index 5775e8d..d28a0b7 100644
--- a/src/timesfm/timesfm_torch.py
+++ b/src/timesfm/timesfm_torch.py
@@ -46,6 +46,7 @@ def __post_init__(self):
     self.global_batch_size = self.per_core_batch_size
     self._device = torch.device("cuda:0" if (
         torch.cuda.is_available() and self.backend == "gpu") else "cpu")
+    self._median_index = -1
 
   def load_from_checkpoint(
       self,
@@ -67,14 +68,13 @@ def load_from_checkpoint(
     self._model.eval()
     # TODO: add compilation.
 
-  def forecast(
+  def _forecast(
       self,
       inputs: Sequence[Any],
       freq: Sequence[int] | None = None,
       window_size: int | None = None,
       forecast_context_len: int | None = None,
       return_forecast_on_context: bool = False,
-      truncate_negative: bool = False,
   ) -> tuple[np.ndarray, np.ndarray]:
     """Forecasts on a list of time series.
 
@@ -89,8 +89,6 @@ def forecast(
           forecast_context_len: optional max context length.
           return_forecast_on_context: True to return the forecast on the context
             when available, i.e. after the first input patch.
-          truncate_negative: truncate to only non-negative values if all the contexts
-            have non-negative values.
 
         Returns:
         A tuple for JTensors:
@@ -110,7 +108,6 @@ def forecast(
     else:
       fcontext_len = forecast_context_len
     inputs = [np.array(ts)[-fcontext_len:] for ts in inputs]
-    inp_min = np.min([np.min(ts) for ts in inputs])
 
     if window_size is not None:
       new_inputs = []
@@ -166,7 +163,4 @@ def forecast(
     if window_size is not None:
       mean_outputs = mean_outputs[0::2, ...] + mean_outputs[1::2, ...]
       full_outputs = full_outputs[0::2, ...] + full_outputs[1::2, ...]
-    if inp_min >= 0 and truncate_negative:
-      mean_outputs = np.maximum(mean_outputs, 0.0)
-      full_outputs = np.maximum(full_outputs, 0.0)
     return mean_outputs, full_outputs