updated for jupyter book

2021-02-22 02:56:53 +08:00 · 2021-02-22 02:56:53 +08:00 · f9f43f073b
parent 71c12c9e18
commit f9f43f073b
10 changed files with 125 additions and 125 deletions
--- a/01_AutoRegressiveIntegratedMovingAverage/01_AutoRegressiveIntegratedMovingAverage.ipynb
+++ b/01_AutoRegressiveIntegratedMovingAverage/01_AutoRegressiveIntegratedMovingAverage.ipynb
@ -1570,7 +1570,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.7.4"
  }
 },
 "nbformat": 4,
--- a/04_GrangerCausality/04_GrangerCausality.ipynb
+++ b/04_GrangerCausality/04_GrangerCausality.ipynb
@ -75,9 +75,18 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/prince.javier/opt/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+      "  import pandas.util.testing as tm\n"
+     ]
+    }
+   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
@ -101,7 +110,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
@ -239,13 +248,13 @@
       "[7116 rows x 4 columns]"
      ]
     },
-     "execution_count": 74,
+     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
-    "data = pd.read_csv('data/Ipo_dataset.csv', index_col='Time');\n",
+    "data = pd.read_csv('../data/Ipo_dataset.csv', index_col='Time');\n",
    "data = data.dropna()\n",
    "data"
   ]
@ -909,7 +918,7 @@
    }
   ],
   "source": [
-    "data = pd.read_csv('data/La Mesa_dataset.csv', index_col='Time');\n",
+    "data = pd.read_csv('../data/La Mesa_dataset.csv', index_col='Time');\n",
    "data = data.dropna()\n",
    "data = data.drop(['Rain', 'ONI'], axis=1)\n",
    "# data['Rain'] = data['Rain'] - data['Rain'].shift(1)\n",
@ -1113,7 +1122,7 @@
    }
   ],
   "source": [
-    "jena_data = pd.read_csv('data/jena_climate_2009_2016.csv')\n",
+    "jena_data = pd.read_csv('../data/jena_climate_2009_2016.csv')\n",
    "jena_data['Date Time'] = pd.to_datetime(jena_data['Date Time'])\n",
    "jena_data = jena_data.set_index('Date Time')\n",
    "jena_data.head(3)"
--- a/05_SimplexandSmapProjections/05_Empirical
+++ b/05_SimplexandSmapProjections/05_Empirical
@ -32,7 +32,7 @@
    "id": "7k0gJqWkg6Le"
   },
   "source": [
-    "# Empirical Dynamic Modelling\n",
+    "## Empirical Dynamic Modelling\n",
    "\n",
    "Natural systems are often complex and dynamic (i.e. nonlinear), making them difficult to understand using linear statistical approaches. Linear approaches are fundamentally based on *correlation*, and thus, they are illposed for dynamical systems, where correlation can occur without causation, and causation may also occur in the absence of correlation.\n",
    "\n",
@ -302,7 +302,7 @@
    "id": "SjJWEJzakQqN"
   },
   "source": [
-    "#  Simplex Projection\n",
+    "##  Simplex Projection\n",
    "\n",
    "\n",
    "- basic idea of simplex forecasting is that even for a chaotic time series, future values may be predicted from the behaviour of similar past values (not possible with random noise)\n",
@ -1347,7 +1347,7 @@
   ],
   "source": [
    "# Read Data and Normalize / Plot\n",
-    "smap_df = pd.read_csv(filepath_or_buffer='./data/ESM2_Data_noise.csv')\n",
+    "smap_df = pd.read_csv(filepath_or_buffer='../data/ESM2_Data_noise.csv')\n",
    "smap_df['R'] = (smap_df['R'] - np.mean(smap_df['R']))/np.std(smap_df['R'])\n",
    "smap_df['L'] = (smap_df['L'] - np.mean(smap_df['L']))/np.std(smap_df['L'])\n",
    "smap_df['T'] = pd.Series(np.arange(1,10001))\n",
@ -1583,7 +1583,7 @@
    "id": "TtYgmhZsyhxb"
   },
   "source": [
-    "# Univariate, Multivariate and Multiview Forecasting of a Resource-Consumer-Predator Model"
+    "## Univariate, Multivariate and Multiview Forecasting of a Resource-Consumer-Predator Model"
   ]
  },
  {
@ -1719,7 +1719,7 @@
    }
   ],
   "source": [
-    "uni_df = pd.read_csv(filepath_or_buffer='./data/5 specie data set.csv', index_col=False)\n",
+    "uni_df = pd.read_csv(filepath_or_buffer='../data/5 specie data set.csv', index_col=False)\n",
    "uni_df['Time'] = pd.Series(np.arange(2000))\n",
    "uni_df = uni_df[['Time', 'R', 'C1', 'C2','P1', 'P2']]\n",
    "uni_df.head()"
@ -2170,7 +2170,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Application to the Jenna Climate Dataset\n",
+    "## Application to the Jenna Climate Dataset\n",
    "\n",
    "We will now try to apply the Simplex Projection and SMap Projection to the Jenna Climate Dataset for 1 variable prediction"
   ]
@ -2203,9 +2203,9 @@
   "outputs": [],
   "source": [
    "# read training data and test data\n",
-    "train_df = pd.read_csv(filepath_or_buffer='./data/train_series.csv')\n",
-    "test_df = pd.read_csv(filepath_or_buffer='./data/test_series.csv')\n",
-    "val_df = pd.read_csv(filepath_or_buffer='./data/val_series.csv')\n",
+    "train_df = pd.read_csv(filepath_or_buffer='../data/train_series.csv')\n",
+    "test_df = pd.read_csv(filepath_or_buffer='../data/test_series.csv')\n",
+    "val_df = pd.read_csv(filepath_or_buffer='../data/val_series.csv')\n",
    "\n"
   ]
  },
@ -2786,7 +2786,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.7.4"
  }
 },
 "nbformat": 4,
--- a/06_ConvergentCrossMappingandSugiharaCausality/ccm_sugihara.ipynb
+++ b/06_ConvergentCrossMappingandSugiharaCausality/ccm_sugihara.ipynb
--- a/06_ConvergentCrossMappingandSugiharaCausality/using_causal_ccm_package.ipynb
+++ b/06_ConvergentCrossMappingandSugiharaCausality/using_causal_ccm_package.ipynb
--- a/07_CrosscorrelationsFourierTransformandWaveletTransform/07_CrosscorrelationsFourierTransformandWaveletTransform.ipynb
+++ b/07_CrosscorrelationsFourierTransformandWaveletTransform/07_CrosscorrelationsFourierTransformandWaveletTransform.ipynb
@ -4,7 +4,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Cross-Correlations, Fourier Transform, and Wavelet Transform\n",
+    "# Chapter 7: Cross-Correlations, Fourier Transform, and Wavelet Transform\n",
    "prepared by Gilbert Chua"
   ]
  },
@ -1934,7 +1934,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Wavelet Transform"
+    "## Wavelet Transform"
   ]
  },
  {
@ -2502,7 +2502,7 @@
    }
   },
   "source": [
-    "# Summary\n",
+    "## Summary\n",
    "\n",
    "\n",
    "To summarize, using these techniques can extract valuable information about time series which are not readily available.\n",
@ -2561,7 +2561,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.4"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
--- a/08_WinningestMethods/lightgbm_jena_forecasting.ipynb
+++ b/08_WinningestMethods/lightgbm_jena_forecasting.ipynb
@ -6,9 +6,7 @@
    "id": "FJogEfYBOfUm"
   },
   "source": [
-    "# Chapter 8: Winningest Methods in Time Series Forecasting\n",
-    "\n",
-    "## Temperature Forecasting (Supplementary Notebook)\n",
+    "# Temperature Forecasting (Supplementary Notebook)\n",
    "\n",
    "Similar to what was done in previous sections, this notebook applies the methodology used in the M5 Forecasting notebook to the Jena Climate dataset. Specifically, we will be forecasting the temperature variable."
   ]
@ -987,7 +985,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.7.4"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
--- a/08_WinningestMethods/lightgbm_m5_forecasting.ipynb
+++ b/08_WinningestMethods/lightgbm_m5_forecasting.ipynb
@ -441,7 +441,6 @@
   ],
   "source": [
    "df_sales.rename(columns = dict(zip(df_sales.columns[6:], date_list)), inplace = True)\n",
-    "\n",
    "df_sales"
   ]
  },
@ -2603,7 +2602,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.4"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
--- a/_config.yml
+++ b/_config.yml
@ -30,3 +30,10 @@ repository:
 html:
  use_issues_button: true
  use_repository_button: true
+
+parse:
+  myst_enable_extensions:
+    # don't forget to list any other extensions you want enabled,
+    # including those that are enabled by default!
+    - amsmath
+    - dollarmath
--- a/_toc.yml
+++ b/_toc.yml
@ -2,7 +2,7 @@
 # Learn more at https://jupyterbook.org/customize/toc.html
 #
 - file: Preface/Preface
-  numbered: true
+  numbered: false
 - file: 00_Introduction/00_Introduction
 - file: 01_AutoRegressiveIntegratedMovingAverage/01_AutoRegressiveIntegratedMovingAverage
 - file: 02_LinearForecastingTrendandMomentumForecasting/02_LinearTrendandMomentumForecasting