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README.md

@@ -18,9 +18,18 @@ Demonstrates timeseries forecasting using a [LSTM model.](https://keras.io/api/l
 
 
 
-## Intended uses & limitations
 
-More information needed
+## Data Preprocessing
+
+Here we are picking ~300,000 data points for training. Observation is recorded every 10 mins, that means 6 times per hour. We will resample one point per hour since no drastic change is expected within 60 minutes. We do this via the sampling_rate argument in timeseries_dataset_from_array utility.
+
+We are tracking data from past 720 timestamps (720/6=120 hours). This data will be used to predict the temperature after 72 timestamps (72/6=12 hours).
+
+Since every feature has values with varying ranges, we do normalization to confine feature values to a range of [0, 1] before training a neural network. We do this by subtracting the mean and dividing by the standard deviation of each feature.
+
+71.5 % of the data will be used to train the model, i.e. 300,693 rows. split_fraction can be changed to alter this percentage.
+
+The model is shown data for first 5 days i.e. 720 observations, that are sampled every hour. The temperature after 72 (12 hours * 6 observation per hour) observation will be used as a label.
 
 ## Training and evaluation data