LITM (not to be confused with the bar and art gallery in Jersey City) is a fictional company created for the purposes of this prompt. LITM is an innovative technology company that specializes in developing cutting-edge software solutions for various industries. With a team of highly skilled software engineers, designers, and project managers, LITM strives to tackle complex problems and deliver customized software solutions that meet the needs of their clients. Their expertise lies in areas such as artificial intelligence, data analytics, machine learning, and software development. LITM aims to revolutionize industries by leveraging technology to improve efficiency, streamline processes, and drive overall growth.
Cost analysis of stocks LITM
Cost analysis of stocks is the process of assessing the current and potential value of a company’s stocks in the stock market.
| P/E – | P/B 0.37 | P/S – | PEG – |
| – | The company is undervalued – Buy | – | – |
Forecasting using signals from 55 indicators
| Sell – 2 | Hold – 41 | Buy – 12 |
Forecasting the price trend LITM
Linear regression
Forecasting LITM using linear regression. Linear regression is a statistical method used to forecast the values of one variable based on another, assuming that there is a linear relationship between them. This method is often used in machine learning to predict values based on statistical data. 
Holt
Forecasting LITM using the Holt model (double exponential smoothing) is a method for predicting time series data that takes into account both the level and trend of the data. This model is often used to address forecasting tasks, especially when the time series exhibits a trend. 
Forecasting LITM using Prophet Forecasting using Prophet is a time series forecasting methodology developed by engineers at Facebook. It is based on the principles of hybrid models (detailed in their published paper) and incorporates seasonal components. Prophet is a powerful tool for analyzing and forecasting time series data, including sales data, financial metrics, weather data, and more. It can be used for forecasting both short and long time periods, as well as for incorporating seasonality and holiday effects. 
Forecasting LITM using 7 mathematical models
SimpleExpSmoothing (SES)
SimpleExponentialSmoothing (SES) is an exponential smoothing method used for time series forecasting. The SES method is a simple and popular tool for generating short-term forecasts. It is particularly useful for data without a clear trend or seasonality, as it is designed to capture the base level or average value of the time series and adjust for new observations. 
Autoregressive Model (AR)
Forecasting LITM using Autoregressive Model (AR) is a time series forecasting method based on the assumption that future values of the time series can be predicted by combining past values of the same series. 
Autoregressive Integrated Moving Average (ARIMA)
Forecasting LITM using ARIMA (Autoregressive Integrated Moving Average) is a time series modeling method that combines autoregression (AR), differencing (I), and moving average (MA). The ARIMA model enables forecasting of future values of time series, as well as analyzing their structure and underlying patterns. 
auto-ARIMA
Auto ARIMA (Automatic Autoregressive Integrated Moving Average) is a time series forecasting tool that automatically tunes the parameters of the ARIMA model based on the input data.
Theta
Forecasting LITM using Theta model (Theta forecasting) is a method of time series forecasting that utilizes a stochastic process known as the Theta (θ) model. The Theta model was developed to enhance time series forecasting by accounting for trend dynamics and reducing the influence of significant deviations at the initial time period. 
ESF
A variation of the simple moving average model.
SARIMAX
Forecasting LITM using the SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors) model is a method of time series forecasting that takes into account both seasonality and the influence of external factors (exogenous variables) on the studied time series. When using the SARIMAX model, specific characteristics of the time series as well as external factors that may affect its behavior are analyzed. 
Forecasting profitability LITM
The Autoregressive Conditional Heteroskedasticity (ARCH) model is used to forecast the volatility of financial time series. It is employed to predict the volatility of the return on a financial instrument.
