Alright, buckle up, because I’m about to spill the beans on my latest side project: trying to predict the future… kind of. I called it “what sign is future” because, well, it sounded cooler than “yet another time series analysis thingy.”
First things first: Data, data, data!
I started by grabbing historical stock prices. I figured, hey, if anyone can predict the future, it’s gotta be the stock market, right? Wrong, probably, but it was a place to start. I scraped data from Yahoo Finance (don’t tell them!). I focused on a few major tech companies – you know, the usual suspects: Apple, Google, Amazon. I pulled about 5 years’ worth of daily open, high, low, close, and volume data. CSV files galore!
Cleaning up the Mess
Let me tell you, raw data is UGLY. Missing values everywhere! I spent a good chunk of time filling in those gaps. Mostly I used simple forward fill or backward fill, figuring it wouldn’t make a HUGE difference. Then, I normalized the data. Min-max scaling, baby! Got everything between 0 and 1. Looked much prettier.
Model Time: Let’s get Recurrent!
I decided to go with an LSTM (Long Short-Term Memory) network. Why? Because everyone says they’re good for time series stuff. Honestly, it sounded fancy. I used TensorFlow/Keras, which I’m semi-familiar with. I built a simple model: LSTM layer, then a dense layer for the prediction. Nothing too wild.
- LSTM layer: 50 units
- Dropout: 0.2 (to prevent overfitting, allegedly)
- Dense layer: 1 unit (predicting the next day’s closing price)
Training, Training, Little Model is Training…
I split the data into training and testing sets (80/20 split). Trained the model for like, 50 epochs. Used Adam optimizer, because, again, sounded good. Watched the loss go down. Felt smart. Probably wasn’t. It was noisy data though, so loss reduction was slow.
The Moment of Truth: Prediction Time!
I fed the test data into the model and got some predictions. Looked like… squiggles. I plotted the predicted prices against the actual prices. They were… vaguely similar? Kind of? Mostly just squiggles that went up and down around the same time. The actual values though? They’re miles apart.
Reality Check: It Sucks!
Okay, so it wasn’t predicting the future. Shocker. The Mean Squared Error was awful. The predictions were basically useless for making any real-world decisions. But hey, I learned a few things!
Lessons Learned (and what I’d do differently)
- Data is KING. I need more data, better data, and maybe some other features besides just price and volume. News sentiment? Economic indicators? Who knows!
- LSTM might not be the answer. Maybe a Transformer model? Or something even fancier.
- Hyperparameter tuning is a MUST. I just used some default values. Need to play around with learning rates, batch sizes, etc.
- Stop thinking about predicting the FUTURE! Focus on maybe understanding trends instead.
Next Steps
I’m not giving up yet! I’m gonna try adding more features, tweaking the model, and maybe even exploring different algorithms. I might also try predicting something simpler, like whether the price will go up or down tomorrow (a binary classification problem). Baby steps, people, baby steps.
Conclusion
So, can I predict the future? Nope. Not even close. But it was a fun little experiment, and I learned a lot about time series analysis, LSTMs, and the general difficulty of making accurate predictions. Stay tuned for the next iteration… it might be slightly less terrible!