Update README.md

README.md

@@ -4,18 +4,31 @@ tags:
 - timeseries
 ---
 
+# Timeseries classification from scratch
+
+Based on the _Timeseries classification from scratch_ example on [keras.io](https://keras.io/examples/timeseries/timeseries_classification_from_scratch/) created by [hfawaz](https://github.com/hfawaz/).
+
 ## Model description
 
-More information needed
+The model is a Fully Convolutional Neural Network originally proposed in [this paper](https://arxiv.org/abs/1611.06455).
+The implementation is based on the TF 2 version provided [here](https://github.com/hfawaz/dl-4-tsc/).
+The hyperparameters (kernel_size, filters, the usage of BatchNorm) were found via random search using [KerasTuner](https://github.com/keras-team/keras-tuner).
 
 ## Intended uses & limitations
 
-More information needed
+Given a time series of 500 samples, the goal is to automatically detect the presence of a specific issue with the engine.
+
+The data used to train the model was already _z-normalized_: each timeseries sample has a mean equal to zero and a standard deviation equal to one.
 
 ## Training and evaluation data
 
-More information needed
+The dataset used here is called [FordA](http://www.j-wichard.de/publications/FordPaper.pdf). The data comes from the [UCR archive](https://www.cs.ucr.edu/%7Eeamonn/time_series_data_2018/). The dataset contains:
 
+ - 3601 training instances
+ - 1320 testing instances
+ 
+ Each timeseries corresponds to a measurement of engine noise captured by a motor sensor.
+ 
 ## Training procedure
 
 ### Training hyperparameters
@@ -33,4 +46,9 @@ The following hyperparameters were used during training:
 
 ![Model Image](./model.png)
 
-</details>
+</details>
+
+
+<center>
+Model reproduced by <a href="https://github.com/EdAbati" target="_blank">Edoardo Abati</a>
+</center>