Update README.md

README.md

@@ -10,7 +10,7 @@ datasets:
 **Owner:** DataBloom AI, Inc.
 
 ### Model Overview ###
-LSTEnergy [last energy] is a Long-Term-Short-Memory model to predict energy consumption forecasts based on historical data. It basically takes 
+LSTEnergy [last energy] is a Long short-term memory model to predict energy consumption forecasts based on historical data. It basically takes 
 some smartmeter data (5 cols, > 12mil. instances, cols: id, device_name, property, value, timestamp) and creates a custom forecast based on selected window. 
 
 Please notice that once you load up the smartmeter data, there are inputs created on the timestamp col like wd_input (the weekday of the timestamp), as well as a cos(inus) and sin(us)
@@ -21,9 +21,9 @@ After that, some functions are used to give the user the ability to use time win
    
 The first models created are a simple baseline model, used for evaluating the performance of the later on built LTSM model. The baseline model simply shifts the values by t=1. Hence,
 there is no t=0 and each timestamp uses the value from t-1.
-Finally, there's the 2-layer plain vanilla LTSM. After 11 epochs, I reached a loss of 10.86 which is rather mediocre. However, the main idea here is to build a basic forecasting model
+Finally, there's the 2-layer plain vanilla LSTM. After 11 epochs, I reached a loss of 10.86 which is rather mediocre. However, the main idea here is to build a basic forecasting model
 for which this seems appropriate.   
 
-![LTSM](https://github.com/databloom-ai/LLM-LTSM/blob/main/LSTEnergy/LTSM.png?raw=true)
+![LSTM](https://github.com/databloom-ai/LLM-LTSM/blob/main/LSTEnergy/LTSM.png?raw=true)
 
 ***Happy Hacking!***