Update docs/images.

Author: zhangxjohn

README.md

@@ -1,8 +1,19 @@
-# HyperTS
+# Welcome to HyperTS
+
 Easy-to-use, powerful, unified full pipeline automated time series toolkit. Supports forecasting, classification, regression and others.
 
+## Overview
+HyperTS is a Python package that provides an end-to-end time series (TS) analysis toolkit. It covers complete and flexible AutoML workflows for TS, including data clearning, preprocessing, feature engineering, model selection, hyperparamter optimization, result evaluation, and visualization.
+
+Multi-mode drive, light-heavy combination is the highlight feature of HyperTS. Therefore, statistical models (STATS), deep learning (DL), and neural architecture search (NAS) can be switched arbitrarily to get a powerful TS estimator.
+
+Easy-to-use and lower-level API. Users can get a model after simply running the experiment, and then execute ```.predict()```, ```.predict_proba()```, ```.evalute()```, ```.plot()``` for various time series analysis.
+
+
 ## Examples
 
+Users can quickly create and ```run()``` an experiment with ```make_experiment()```, where ```train_data```, and ```task``` as required input parameters. In the forecast task in the following example, we tell the experiment that it is a multivariate-forecast ```task```, using stats ```mode```, since the data contains timestamp and covariable columns, ```timestamp``` and ```covariables``` parameters might inform the experiment.
+
 ```python
 from hyperts.experiment import make_experiment
 from hyperts.datasets import load_network_traffic
@@ -13,12 +24,10 @@ data = load_network_traffic()
 train_data, test_data = train_test_split(data, test_size=0.2, shuffle=False)
 
 model = make_experiment(train_data.copy(),
-                        task='forecast',
-                        mode='dl',
-                        forecast_window=24*2,
+                        task='multivariate-forecast',
+                        mode='stats',
                         timestamp='TimeStamp',
-                        covariables=['HourSin', 'WeekCos', 'CBWD'],
-                        max_trials=100).run()
+                        covariables=['HourSin', 'WeekCos', 'CBWD']).run()
 
 X_test, y_test = model.split_X_y(test_data.copy())
 
@@ -29,6 +38,31 @@ scores = model.evaluate(y_test, y_pred)
 model.plot(forecast=y_pred, actual=test_data, var_id=0)
 ```
 
+<div align="center"><img src="docs/static/images/Actual_vs_Forecast.jpg" width="800"/></div>
+
+- More detailed guides: [EXAMPLES.](https://github.com/DataCanvasIO/HyperTS/tree/main/examples)
+
+## Key Features
+
+HyperTS supports the following features:
+
+**Multi-task Support:** Time series forecasting, classification, and regression.
+
+**Multi-mode Support:** A large collection of TS models, from statistical models to deep learning models, and to neural architecture search (developing).
+
+**Multi-variate Support:** From univariate to multivariate time series.
+
+**Covariates Support:** Deep learning models support covariates as input featues for time series forecasting. 
+
+**Probabilistic intervals Support:** Time series forecsting visualization can show confidence intervals.
+
+**Abundant Metrics:** From ```MSE```,``` SMAPE```, ```Accuracy``` to ```F1-Score```, a variety of performance metrics to evaluate results and guide models optimization.
+
+**Powerful search strategies:** Adapting Grid Search, Monte Carlo Tree Search, Evolution Algorithm combined with a meta-learner to learn a powerful and effective pipeline.
+
+## Communication
+If you wish to contribute to this project, please refer to [CONTRIBUTING](CONTRIBUTING.md).
+
 ## DataCanvas
 
 ![datacanvas](docs/static/images/dc_logo_1.png)

docs/static/images/Actual_vs_Forecast.jpg

@@ -6,4 +6,5 @@ numpy>=1.16.5, <1.19.4
 pandas>=0.25.3
 scikit-learn>=0.22.1
 tensorflow>=2.0.0
-plotly>=4.12.0
+plotly>=4.12.0
+pyparsing==2.4.7