Setting median outputs as default. Also minor changes to finetuning notebook.

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)}\")"