usairamsaeed commited on
Commit
b9e80b4
·
verified ·
1 Parent(s): 255ff9d

Upload algoDinero_gru_forecaster model - 2025-10-07 01:57:39

Browse files
Files changed (2) hide show
  1. README.md +78 -0
  2. model.pkl +3 -0
README.md ADDED
@@ -0,0 +1,78 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ tags:
4
+ - fintech
5
+ - forecasting
6
+ - time-series
7
+ - financial
8
+ - gru
9
+ ---
10
+
11
+ # Algodinero Gru Forecaster - Financial Time Series Forecasting Model
12
+
13
+ ## Model Description
14
+
15
+ This is a GRU neural network model trained for financial time series forecasting. The model is part of the AlgoDinero FinTech Data Curation project and is designed to predict future stock prices and cryptocurrency values.
16
+
17
+ ## Model Architecture
18
+
19
+ - **Model Type**: GRU
20
+ - **Input**: Time series of closing prices
21
+ - **Output**: Forecasted prices for future time steps
22
+ - **Training Data**: Multi-asset financial data (stocks and cryptocurrencies)
23
+
24
+ ## Usage
25
+
26
+ ```python
27
+ import torch
28
+ import numpy as np
29
+ from huggingface_hub import hf_hub_download
30
+
31
+ # Download model
32
+ model_path = hf_hub_download(repo_id="usairamsaeed/algoDinero_gru_forecaster", filename="model.pkl")
33
+
34
+ # Load model
35
+ with open(model_path, 'rb') as f:
36
+ model_data = pickle.load(f)
37
+
38
+ # Use with your forecasting pipeline
39
+ ```
40
+
41
+ ## Training Details
42
+
43
+ - **Training Period**: 365 days of historical data
44
+ - **Assets**: AAPL, TSLA, BTC-USD, ETH-USD, MSFT, GOOGL
45
+ - **Data Preprocessing**: Normalized using z-score normalization
46
+ - **Training Method**: Adam optimizer with MSE loss
47
+
48
+ ## Model Performance
49
+
50
+ This model is part of an ensemble forecasting system that combines multiple approaches:
51
+ - Moving Average
52
+ - ARIMA
53
+ - VAR
54
+ - MLP
55
+ - GRU
56
+ - LSTM
57
+ - Transformer
58
+
59
+ The ensemble approach provides robust predictions by combining the strengths of different forecasting methods.
60
+
61
+ ## Limitations
62
+
63
+ - Model performance depends on market conditions
64
+ - Historical performance does not guarantee future results
65
+ - Use for educational/research purposes
66
+ - Not intended for actual trading decisions
67
+
68
+ ## Citation
69
+
70
+ ```
71
+ @misc{algoDineroGru,
72
+ title={AlgoDinero GRU Financial Forecasting Model},
73
+ author={Usair Ahmad Saeed},
74
+ year={2025},
75
+ publisher={Hugging Face},
76
+ howpublished={\url{https://huggingface.co/usairamsaeed/algoDinero_gru_forecaster}}
77
+ }
78
+ ```
model.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:11e93b4d3779fe492e21c8ebd5c103944efebf525410aa5b1758980c10e7b700
3
+ size 15660