{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "ZrwVQsM9TiUw" }, "source": [ "##### Copyright 2022 The TensorFlow Probability Authors.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "id": "CpDUTVKYTowI" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "AoIafJcKlM-R" }, "source": [ "# 结构时间序列建模案例研究:大气 CO2 和电力需求\n", "\n", "此笔记本说明了将结构时间序列模型拟合到时间序列并使用它们生成预测和解释的两个示例。" ] }, { "cell_type": "markdown", "metadata": { "id": "Qianaf6u_7G_" }, "source": [ "
![]() | \n",
" ![]() | \n",
" ![]() | \n",
" ![]() | \n",
"