Univariant Timeseries Forecasting

Motivation: A little Browser Playground for using LSTM layers.
Sources: TF Timeseries Tutorial and TF MaunaLoa CO2

Training Data

Generate with n =

Load Data

[optional] Only take last timesteps from

Normalized Input Data

Clip to min= max=



Stride Timeseries

(n) Train / Test Split ( of n)
(w) History Window
(f) Forecast Steps
dim x = [n - (w - 1), w]



Train Data:

Test Data:


Preview of Timeseries X and Target Value Y (Test Dataset):

Preview Index: 0



Create Tensor's and Shuffle Data

Note: Add new Dimension of shape from [n, m] to [n, m, 1]

Shuffle Data



Create Network








Train Model

Hyperparameter Settings:




Test Accurracy

Test Model on unseen data (Train/Test Split):

Test Data Index: 0



Forecast

on the overall CO2 Data.

Continue Forecasting on predicted data: Timespan +1