Upwork is hiring a Time series analysis

As forecasting problem where the part of the main prediction which are chess steps has been already done in a train data set. Also the first noise predictions between the steps has been done. The goal would be to look if the noise between the steps which will most probably also depend on the chess steps before be predictable.

Furthermore finally baseline evaluation has to be done

Data attached: dtwin.ipynb -- Program so far

train_new.npy -- train data set

Slides on evaluation and also digital twins attached.

(original task: Available data

12220 historical states (training set)

You need to build a digital model from the data

ARIMA, XGBoost, LSTM, Variational Autoencoder, Support

Vector Machine, Linear regression, home-brewed model, . . . ?

Prediction task

You need to predict 10660 future states of the physical twin

Secret test set not available

Available Domain Information:

You don’t know much about the physical twin

Goal: Focus on KDDM methods instead of using general knowledge

Some information available

State matrix indices refer to locations→ spatial data

True states are obscured by noise

12220 states are sorted chronologically

Further Note on Difficulty

The prediction task is not directly solvable

Major components of spatio-temporal process are not predictable

→ Need human intellect to solve problem

→ Need to build good digital model to help human

Solution Example (not the true task) I

1. You receive historical states

2. You remove all noise using Butterworth filter

3. You use PCA to find three important features

4. You visualize these features in a 3d plot

Human realizes that these are x, y, z positions

Human observes regular rotation

5. You detect that the rotation period is 3600 states

6. You realize you are observing a clock’s minute hand

7. You realize that forecasting the future state is trivial

Recommendations:

Focus effort on pre-processing, decomposition, stationarity, etc.

Use many visualizations)