Tesla Inc (TSLA)
🚧 Work in Progress 🚧
Deep learning enables accurate time series forecasting by capturing complex time dependencies through neural networks' innate understanding of time relationships between data points and interpolation in high-dimensional spaces using continuous activation functions.
| Dataset | Input Feat. | AI | Output | Short Forms |
|---|---|---|---|---|
| Daily TSLA historical data from MONTH DD, 2003 to MONTH DD, 2023. | 'date', 'open', 'high', 'low', 'close', 'volume', 'ema', 'dema', 'sma', 'standardDeviation', 'tema', 'williams', 'wma' | PyTorch Forecasting with NHiTS for multi-scale interpolation and synchronized frequencies. Evaluated with MAE, SMAPE, MQF2DistributionLoss, and QuantileLoss. GPU accelerated. | Predictive time series data for TSLA from MONTH DD, 2023 to MONTH DD, 2023. | Actual Closing Price (ACP), Predicted Closing Price (PCP), Percent Difference (% diff) = [|ACP-PCP|/(ACP+PCP)]*100 |
Comparative Table
| Date | ACP | PCP | % diff |
|---|---|---|---|
| 🚧 Work in Progress 🚧 | 🚧 Work in Progress 🚧 | 🚧 Work in Progress 🚧 | 🚧 Work in Progress 🚧 |