Got it, that makes sense! By standardizing each input independently, you mean each dimension right? If I have OHLCV data, does it make sense to standardize the OHLC data together and the V data separately?
Recommendations on time-series price prediction models?
- Thread starter jublin
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Got it, that makes sense! By standardizing each input independently, you mean each dimension right? If I have OHLCV data, does it make sense to standardize the OHLC data together and the V data separately?
If those are your only inputs, then yes, you could do OHLC together, or separately. (Yes, by 'input,' I mean dimension.) And I would definitely do V separately, in either case.
For my program, I used the raw, unprocessed data from column 7 of inputData.csv in
https://www.elitetrader.com/et/thre...-prediction-models.357511/page-3#post-5368622
I didn't want to normalize or standardize the data because I wanted to fit the actual data. And I've never found volume too useful for machine learning problems because it varies too much.
My x values were offsets in number of days from the input data (0 through 88 for the data I posted for fitting; > 88 for predicting the future).
It's possible your genetic optimization for function minimization implementation
- Trained with too few or too many generations.
- Had a too small or too large population size.
- Used a crossover that mixed data of different types (e.g., y intercept of a parabola and frequency of a cosine wave probably shouldn't be crossed over).
- Initialized and/or mutated parameter values that had no chance of helping (e.g., using a value of 123456789 when the input data was orders of magnitude away from that).
- Used a pseudorandom number generator that wasn't very random or had too short a period. I use WELLRNG44497 (ported from http://www.ritsumei.ac.jp/~harase/WELL44497a_new.c).
Another thing that could help is using a larger input data size. I've been using 123 calendar days instead of the 89 I posted in this thread because the results seem more consistent among different runs on assorted assets.
Regarding other function minimizers/sinusoid fitters, I tried
- Downhill simplex method (aka Nelder-Mead method aka Amoeba) ported from https://metacpan.org/pod/Math::Amoeba
- Powell's Convergent Method of parameter minimization. ported from "Numerical Recipes in C," William H Press, et al.
- Prony's method ported from "A tutorial on trigonometric curve fitting" By Cedrick Collomb http://www.emptyloop.com/technotes/A tutorial on trigonometric curve fitting.pdf
- Fourier transform ported from "The Scientist and Engineer's Guide to Digital Signal Processing" By Steven W. Smith, Ph.D., http://www.dspguide.com/pdfbook.htm and http://www.dspguide.com/programs.txt
So then I tried function minimization by genetic optimization and later linear genetic programming. I currently use the genetic optimization method because my genetic programming implementation produced about the same results but ran slower.
By the way, I went through the post and confirm that it predicts one day ahead. It uses the last 60 days of data to predict the next day:
Code:
for i in range(60, 1482):
X_train.append(training_set_scaled[i-60:i, 0])
y_train.append(training_set_scaled[i, 0])Here is an article that has examples of multi-step LSTM forecasting.
https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/
And another by the same author that mentions other ways to do multi-step forecasting.
https://machinelearningmastery.com/multi-step-time-series-forecasting/
His book could be helpful too.
https://machinelearningmastery.com/introduction-to-time-series-forecasting-with-python/
This is pretty bad. Sanity test: if the predictions look like the original data shift back some time your just using the previous price as your prediction for the next price. which is basically modelling it as a stochastic process with no useful signal...
