/TESLA-stock-price-forecasting

Time series forecasting techniques to predict TESLA's stock price

Primary LanguageJupyter Notebook

TESLA-stock-price-forecasting

Topic

In this notebook I have gone throught the last year of Tesla's stock prices to understand that they have a downtrend over time and a seasonality that decreases over time. With the help of three time series forecasting models (ETS, SARIMA and Prophet) I was able to make predictions and compare my results cross model. For my ETS model I used and addutive method for both trend and seasonality, for the SARIMA model I used some AR and MA terms plus the differencing terms and with Prophet I accounted for the holidays seasonality in the data. The results lead me towards my ETS model : Holt Winter's seasonal trend which yielded the lowest errors and lowest AIC score. I then used my tuned ETS model to make forecasts of January 2023 which indicate yet a loss in the value of Tesla's stock options.

Models

  • ETS: Holt Winter's Seasonal method
  • ARIMA : Seasonal ARIMA
  • Prophet

Evaluation Metrics

  • Mean Absolute Error
  • Root Mean Squared Error
  • Akaike Information Criterion

Libraries

  • numpy
  • pandas
  • scipy
  • sklearn
  • prophet
  • statsmodels

Data source

https://finance.yahoo.com/quote/TSLA/history?p=TSLA