Sliding windows can make any algorithms become Online disaggregate?

[Hessen525]

Hi, @OdysseasKr
Thanks for your nice paper and reply, it make me learn a lot! I have some question as following:

1. What is the meaning of online(real-time)application? As long as we use past time steps for now, we can call online？ (ex.(x1,x2,x3…… xt) ----> yt ?)
2. If Q1 is correct. Do you think sliding windows make any algorithms become online because we can reshape the data.
   (ex. Y_batch = meterchunk[w-1+offset:w-1+offset+batch_size]
   X_batch = np.reshape(X_batch, (len(X_batch), w ,1))

Thanks again, any reply and advice will be much appreciated!

[OdysseasKr]

1. The meaning of online(real-time) is that we do not require the whole time-series in order to create predictions. The model is trained on past data and remains as it is. When it is time to disaggregate new data, a buffer of size w (window size) is required. This is where we store the last w values in order to create a new prediction. In that sense, the models can be used online as they only use the real-time feed of data.

2. Reshaping the data essentially re-creates that scenario of having an input of size w and an output of size 1. In a real-life case, we wouldn't wait for all of the data to arrive and then reshape them. Instead, the disaggregation takes place as new data samples are measured.

[Hessen525]

Thanks so much for your help, @OdysseasKr
Is that means we can make any approaches to online approaches by sliding window based on past data ? like BiLSTM is not online because it use both past and future data, by reshape and using sliding windows based on past data, it can be an online approach.?