update tutorial about timeF encoding

tutorial/TimesNet_tutorial.ipynb

@@ -194,7 +194,7 @@
     "\n",
     "    # find period by amplitudes: here we assume that the peroidic features are basically constant\n",
     "    # in different batch and channel, so we mean out these two dimensions, getting a list frequency_list with shape[T] \n",
-    "    # each element at pos t of frequency_list demeans the overall amplitude at frequency (t)\n",
+    "    # each element at pos t of frequency_list denotes the overall amplitude at frequency (t)\n",
     "    frequency_list = abs(xf).mean(0).mean(-1) \n",
     "    frequency_list[0] = 0\n",
     "\n",
@@ -1315,7 +1315,7 @@
     "          options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h')\n",
     "         So you should check the timestep of your data and set 'freq' arg. \n",
     "         After the time_features encoding, each date info format will be encoded into \n",
-    "         a list, with each element demeaning the relative position of this time point\n",
+    "         a list, with each element denoting the relative position of this time point\n",
     "         (e.g. Day of Week, Day of Month, Hour of Day) and each normalized within scope[-0.5, 0.5]\n",
     "         '''\n",
     "        data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq)\n",