diff --git a/notebooks/05 Timeseries Forecasting.ipynb b/notebooks/05 Timeseries Forecasting.ipynb index 22e36cf0..805a361d 100644 --- a/notebooks/05 Timeseries Forecasting.ipynb +++ b/notebooks/05 Timeseries Forecasting.ipynb @@ -215,7 +215,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "we now have 1252 unique windows (identified by stock symbol and ending date):" + "we now have 1253 unique windows (identified by stock symbol and ending date):" ] }, { @@ -342,7 +342,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Just to repeat: the features in this row were only calculated using the time series values of `AAPL` up to and including `2015-07-14` and the last 20 days." + "Just to repeat: the features in this row were only calculated using the time series values of `AAPL` up to and including `2020-07-14` and the last 20 days." ] }, { @@ -418,7 +418,7 @@ "source": [ "We can now train normal AdaBoostRegressors to predict the next time step .\n", "Let's split the data into a training and testing sample (but make sure to keep temporal consistency).\n", - "We take everything until 2019 as train data an the rest as test:" + "We take everything until 2020 as train data an the rest as test:" ] }, { @@ -427,7 +427,7 @@ "metadata": {}, "outputs": [], "source": [ - "X[:\"2018\"]" + "X[:\"2020\"]" ] }, { @@ -436,11 +436,11 @@ "metadata": {}, "outputs": [], "source": [ - "X_train = X[:\"2018\"]\n", - "X_test = X[\"2019\":]\n", + "X_train = X[:\"2020\"]\n", + "X_test = X[\"2021\":]\n", "\n", - "y_train = y[:\"2018\"]\n", - "y_test = y[\"2019\":]" + "y_train = y[:\"2020\"]\n", + "y_test = y[\"2021\":]" ] }, { @@ -535,7 +535,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/notebooks/advanced/05 Timeseries Forecasting (multiple ids).ipynb b/notebooks/advanced/05 Timeseries Forecasting (multiple ids).ipynb index 56e94979..c3e8fc56 100644 --- a/notebooks/advanced/05 Timeseries Forecasting (multiple ids).ipynb +++ b/notebooks/advanced/05 Timeseries Forecasting (multiple ids).ipynb @@ -197,7 +197,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Just to repeat: the features in this row were only calculated using the time series values of `AAPL` up to and including `2015-07-15` and the last 20 days." + "Just to repeat: the features in this row were only calculated using the time series values of `AAPL` up to and including `2020-07-14` and the last 20 days." ] }, { @@ -268,11 +268,11 @@ "metadata": {}, "outputs": [], "source": [ - "X_train = X.loc[(slice(None), slice(None, \"2018\")), :]\n", - "X_test = X.loc[(slice(None), slice(\"2019\", \"2020\")), :]\n", + "X_train = X.loc[(slice(None), slice(None, \"2020\")), :]\n", + "X_test = X.loc[(slice(None), slice(\"2021\", \"2022\")), :]\n", "\n", - "y_train = y.loc[(slice(None), slice(None, \"2018\"))]\n", - "y_test = y.loc[(slice(None), slice(\"2019\", \"2020\"))]" + "y_train = y.loc[(slice(None), slice(None, \"2020\"))]\n", + "y_test = y.loc[(slice(None), slice(\"2021\", \"2022\"))]" ] }, { @@ -336,6 +336,13 @@ "y.unstack(\"Symbols\").plot(ax=plt.gca())\n", "y_pred.unstack(\"Symbols\").plot(ax=plt.gca(), legend=None, marker=\".\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -354,7 +361,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/notebooks/advanced/visualize-benjamini-yekutieli-procedure.ipynb b/notebooks/advanced/visualize-benjamini-yekutieli-procedure.ipynb index e2949f37..320e08fe 100644 --- a/notebooks/advanced/visualize-benjamini-yekutieli-procedure.ipynb +++ b/notebooks/advanced/visualize-benjamini-yekutieli-procedure.ipynb @@ -38,8 +38,7 @@ "from tsfresh.feature_extraction import ComprehensiveFCParameters\n", "\n", "matplotlib.rcParams[\"figure.figsize\"] = [16, 6]\n", - "matplotlib.rcParams[\"font.size\"] = 14\n", - "matplotlib.style.use('seaborn-darkgrid')" + "matplotlib.rcParams[\"font.size\"] = 14" ] }, {