fixed wrong option description (#2524)

* fixed wrong option description

* add sample picture

* Update demos/time_series_forecasting_demo/python/README.md

Co-authored-by: Zlobin Vladimir <[email protected]>

* Update demos/time_series_forecasting_demo/python/README.md

Co-authored-by: Zlobin Vladimir <[email protected]>

Co-authored-by: Zlobin Vladimir <[email protected]>

Author: vladimir-dudnik

demos/time_series_forecasting_demo/python/README.md

@@ -1,5 +1,7 @@
 # Time Series Forecasting Python\* Demo
 
+![example](./time_series_forecasting_demo.jpg)
+
 This README describes the Time Series Forecasting demo application.
 
 ## How It Works
@@ -10,7 +12,12 @@ The program provides an interactive CLI interface that gets a test dataset in .p
 
 ## Preparing to Run
 
-For demo input image or video files you may refer to [Media Files Available for Demos](../../README.md#Media-Files-Available-for-Demos).
+Install required dependencies with command:
+
+```sh
+pip install -r requirements.txt
+```
+
 The list of models supported by the demo is in `<omz_dir>/demos/whiteboard_inpainting_demo/python/models.lst` file.
 This file can be used as a parameter for [Model Downloader](../../../tools/downloader/README.md) and Converter to download and, if necessary, convert models to OpenVINO Inference Engine format (\*.xml + \*.bin).
 
@@ -26,6 +33,12 @@ An example of using the Model Converter:
 python3 <omz_dir>/tools/downloader/converter.py --list models.lst
 ```
 
+### Supported Models
+
+* time-series-forecasting-electricity-0001
+
+> **NOTE**: Refer to the tables [Intel's Pre-Trained Models Device Support](../../../models/intel/device_support.md) and [Public Pre-Trained Models Device Support](../../../models/public/device_support.md) for the details on models inference support at different devices.
+
 ### Supported datasets
 
 * [Electricity](https://archive.ics.uci.edu/ml/machine-learning-databases/00321/LD2011_2014.txt.zip).
@@ -36,7 +49,8 @@ The demo works with the test dataset in the .pickle format provided by accuracy_
 
 * Install accuracy_checker following to the [instruction](../../../tools/accuracy_checker/README.md).
 * Convert test dataset:
-```
+
+```sh
 wget https://archive.ics.uci.edu/ml/machine-learning-databases/00321/LD2011_2014.txt.zip
 unzip LD2011_2014.txt.zip
 convert_annotation electricity --data_path_file LD2011_2014.txt
@@ -57,17 +71,24 @@ optional arguments:
                         Optional. Name of the models input node.
   --output-name OUTPUT_NAME
                         Optional. Name of the models output node.
-  --data-path DATA_PATH
+  -i INPUT, --input INPUT
                         Required. Path to the dataset file in .pickle format.
   --quantiles QUANTILES
                         Optional. Names of predicted quantiles.
 ```
 
+Running the application with the empty list of options yields the usage message given above and an error message.
+
+You can use the following command to do inference on CPU on the converted dataset using a pre-trained time-series-forecasting-electricity network:
+
+```sh
+python3 time_series_forecasting_demo.py -i electricity.pickle -m <path_to_model>/time-series-forecasting-electricity-0001.xml
+```
+
 ## Demo Output
 
 The application draws predicted quantiles and ground truth curves.
 
-
 ## See Also
 
 * [Open Model Zoo Demos](../../README.md)