Updated README

Signed-off-by: Stefano <stefano.savare@gmail.com>

README.rst

@@ -32,11 +32,11 @@ Time Series Forecasting Model
 Giotto-time provide the GAR class (Generalize Auto Regressive model). It operates in a similar way to the standard AR, but with an arbitrary number of features and with an arbitrary underlying regression model.
 
 
-.. image:: https://storage.googleapis.com/l2f-open-models/giotto-time/images/gar.png
+.. image:: https://storage.googleapis.com/l2f-open-models/giotto-time/images/gar.png?v=400&s=200
   :width: 600
 
 
-This model allows the full force of machine learning regressors (compatible with the fit-transform framework ok scikit-learn) to be combined with advanced feature creation stratagies to forecast time series in a convienent api.
+This model allows the full force of machine learning regressors (compatible with the fit-transform framework of scikit-learn) to be combined with advanced feature creation strategies to forecast time series in a convienent api.
 
 >>> from giottotime.feature_creation import FeaturesCreation
 >>> from giottotime.feature_creation.index_independent_features import ShiftFeature, MovingAverageFeature
@@ -124,10 +124,7 @@ Specifically, giotto-time includes ExponentialTrend, PolynomialTrend model class
 
 |imga| |imgb|
 
-.. |imga| image:: https://storage.googleapis.com/l2f-open-models/giotto-time/images/trend.png
-   :width: 450
-
-.. |imgb| image:: https://storage.googleapis.com/l2f-open-models/giotto-time/images/no_trend.png
+.. |imga| image:: https://storage.googleapis.com/l2f-open-models/giotto-time/images/trends.png
    :width: 450
 
 Before the detrending tranformer, a clear quadratic trend is present in the data. For additional information on trend stationarity, see: Trend stationarity: Wikipedia - https://en.wikipedia.org/wiki/Trend_stationary.
@@ -159,7 +156,10 @@ Causality Tests
 
 We provide two tests: ShiftedLinearCoefficient and ShiftedPearsonCorrelation.
 
-These tests (which are impliemnted as scikit-learn compatible transformers) determine which shift of each time series maximizes the correlation to each other input time series. This is a very similar construction tothe granger test.
+These tests (which are implemented as scikit-learn compatible transformers) determine which shift of each time series maximizes the correlation to each other input time series. This is a very similar construction to the granger test.
+
+.. image:: https://storage.googleapis.com/l2f-open-models/giotto-time/images/granger.png
+  :width: 600
 
 An example use is shown below.
 
@@ -183,6 +183,8 @@ B  0.177856  0.300350  0.367150  0.550490
 C  0.484860  0.263036  0.456046  0.251342
 D  0.580068  0.344688  0.253626  0.256220
 
-The target-col input variable to the constructor is used in the transform method. It determins which set of shifts are applied to all inputs. For example, if 'A' is selected, each column will be transform by a shift corresponding to the 'A' row of the bests_shifts_ pivot table.
+The target-col input variable to the constructor is used in the transform method. It determines which set of shifts are applied to all inputs. For example, if 'A' is selected, each column will be transform by a shift corresponding to the 'A' row of the *best_shifts* pivot table.
+
+
 
 

RELEASE.rst

@@ -1,4 +1,10 @@
-Release 0.1a.0
+Release 0.1.1
 ==============
 
-Initial release of giotto-time.
+Documentation fixes.
+
+Release 0.1.0
+==============
+
+Initial release of giotto-time.
+

giottotime/_version.py

@@ -21,4 +21,4 @@
 # 'X.Y.dev0' is the canonical version of 'X.Y.dev'
 #
 
-__version__ = "0.1.0"
+__version__ = "0.1.1"