Nowcasting is a time series analysis task aiming to forecast the current state of an observed phenomenon. A very short-term forecast is a prediction of a few minutes to a day. And short term refers to forecasting activities where the target is relatively close to the present time and has a relatively short duration, ranging from daily to several months. They rely on recent data and are greatly affected by short-term fluctuations. Long-term and very long-term forecasting, typically encompassing a timeframe exceeding two years, are often undertaken at the strategic level. They have a high degree of uncertainty, and the model relies on assumptions and scenario analysis. The techniques and methods for TSF can be divided into statistical methods, physical methods, time series analysis methods, hybrid methods, and so on. Statistical methods are based on mathematical formulas and probability assumptions, utilizing historical data modeling of time series to capture trends, seasonality, and residual components. They are suitable for short-term data volumes that require fast modeling and high interpretability requirements. [PERSON] (1927) constructed an Autoregressive (AR) model to predict the number of sunspots, which was an earlier TSF model in econometric analysis [2]. After that, scholars further proposed the Moving Average (MA) model to analyze time series data. Slightly different from the AR model, the MA model uses the lag term of the white noise sequence to predict the explained variables. AR and MA models are suitable for stationary time series data. However, it has been found that most financial time series data usually have non-stationary characteristics [3]. For this reason, [PERSON] and [PERSON] further proposed an Autoregressive Integrated Moving Average (ARIMA) model to deal with non-stationary time series data. The ARIMA model is relatively simple and can effectively deal with non-stationary financial time series data, so it has become a commonly used econometric model in the field of financial time series data prediction. Physical methods are based on domain knowledge (physical laws, chemical equations, etc.) to construct mechanistic models, emphasizing causal relationships rather than pure data-driven approaches. They are suitable for scenarios where prediction results must strictly comply with physical laws. For example, weather forecasting uses models based on atmospheric dynamics. The time series analysis methods focus on the decomposition of the intrinsic structure of time series and pattern recognition, emphasizing data-driven feature extraction. It is necessary to have a deep understanding of the internal structure of the sequence, such as trends and cycles. Non-linear Autoregressive (NAR) and Non-linear Autoregressive with Exogenous inputs (NARX) models are prominent examples of time series forecasting techniques that leverage recurrent structures to capture temporal dependencies effectively. NARX models extend traditional autoregressive models by incorporating external input variables, making them especially powerful for complex financial time series forecasting tasks. Hybrid methods in time series forecasting combine multiple forecasting techniques, often blending statistical, machine learning, and/or physical models, to improve predictive accuracy and robustness. The idea behind hybrid methods is to take advantage of the strengths of each individual technique, while compensating for their weaknesses. For example, [PERSON] proposed a hybrid model that combines Ensemble Empirical Mode Decomposition (EEMD), ARIMA, and Taylor expansion using a tracking differentiator to forecast financial time series [4]. Machine learning explores how to improve the performance of the model itself by means of computing and, using learning experience, it tries to find the relationships be tween the input data. [PERSON] et al.