fix links in experiments again

Author: deven367

nbs/docs/experiments/AmazonStatsForecast.ipynb

@@ -46,7 +46,7 @@
    "source": [
     "## Amazon Forecast\n",
     "\n",
-    "Amazon Forecast is a fully automated solution for time series forecasting. The solution can take the time series to forecast and exogenous variables (temporal and static). For this experiment, we used the AutoPredict functionality of Amazon Forecast following the steps of [this tutorial](https://docs.aws.amazon.com/forecast/latest/dg/gs-console.html). A detailed description of the particular steps for this dataset can be found [here](./AmazonStatsForecast).\n",
+    "Amazon Forecast is a fully automated solution for time series forecasting. The solution can take the time series to forecast and exogenous variables (temporal and static). For this experiment, we used the AutoPredict functionality of Amazon Forecast following the steps of [this tutorial](https://docs.aws.amazon.com/forecast/latest/dg/gs-console.html). A detailed description of the particular steps for this dataset can be found [here](./amazonstatsforecast.html).\n",
     "\n",
     "Amazon Forecast creates predictors with AutoPredictor, which involves applying the optimal combination of algorithms to each time series in your datasets.  The predictor is an Amazon Forecast model that is trained using your target time series, related time series, item metadata, and any additional datasets you include. \n",
     "\n",
@@ -70,7 +70,7 @@
    "source": [
     "### Install necessary libraries\n",
     "\n",
-    "We assume you have StatsForecast already installed. Check this guide for instructions on [how to install StatsForecast](../getting-started/0_Installation).\n",
+    "We assume you have StatsForecast already installed. Check this guide for instructions on [how to install StatsForecast](../getting-started/installation.html).\n",
     "\n",
     "Additionally, we will install `s3fs` to read from the S3 Filesystem of AWS. (If you don't want to use a cloud storage provider, you can read your files locally using pandas)"
    ]
@@ -225,7 +225,7 @@
     "\n",
     "We fit the model by instantiating a new `StatsForecast` object with the following parameters:\n",
     "\n",
-    "* `models`: a list of models. Select the models you want from [models](../../models) and import them. For this example, we will use `AutoETS` and `DynamicOptimizedTheta`. We set `season_length` to 7 because we expect seasonal effects every week. (See: [Seasonal periods](https://robjhyndman.com/hyndsight/seasonal-periods/))\n",
+    "* `models`: a list of models. Select the models you want from [models](../../src/core/models.html) and import them. For this example, we will use `AutoETS` and `DynamicOptimizedTheta`. We set `season_length` to 7 because we expect seasonal effects every week. (See: [Seasonal periods](https://robjhyndman.com/hyndsight/seasonal-periods/))\n",
     "\n",
     "* `freq`: a string indicating the frequency of the data. (See [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)\n",
     "\n",

nbs/docs/experiments/ETS_ray_m5.ipynb

@@ -15,7 +15,7 @@
    "id": "664acaef-8fd6-4874-a3ef-ddf32dbbe67d",
    "metadata": {},
    "source": [
-    "In this notebook we show how to use `StatsForecast` and `ray` to forecast thounsands of time series in less than 6 minutes (M5 dataset). Also, we show that `StatsForecast` has better performance in time and accuracy compared to [`Prophet` running on a Spark cluster](./Prophet_spark_m5) using DataBricks.\n",
+    "In this notebook we show how to use `StatsForecast` and `ray` to forecast thounsands of time series in less than 6 minutes (M5 dataset). Also, we show that `StatsForecast` has better performance in time and accuracy compared to [`Prophet` running on a Spark cluster](./prophet_spark_m5.html) using DataBricks.\n",
     "\n",
     "In this example, we used a ray cluster (AWS) of 11 instances of type m5.2xlarge (8 cores, 32 GB RAM)."
    ]