השוואת שיטות

	מודל ARIMA (Autoregressive Integrated Moving Average)	TSMixer: ארכיטקטורת MLP בלבד לחיזוי סדרות עתיות
תחום	אקונומטריקה	למידה עמוקה
משפחה	Regression model	Machine learning
שנת המקור	2015	2023
הוגה השיטה	Box & Jenkins (Box-Jenkins methodology)	Si-An Chen et al. (Google)
סוג	Univariate time-series model	All-MLP multivariate time-series forecasting model
מקור מכונן	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Chen, S.-A., Li, C.-L., Yoder, N., Arik, S. O., & Pfister, T. (2023). TSMixer: An all-MLP architecture for time series forecasting. Transactions on Machine Learning Research.
כינויים	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	All-MLP Time Series Mixer, Time Series Mixer, TSMixer Forecasting Model, Zaman Serisi Karıştırıcı
קשורות	5	3
תקציר	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	TSMixer is a multivariate time-series forecasting model introduced by Si-An Chen and colleagues at Google in 2023. It challenges the prevailing dominance of Transformer-based architectures by demonstrating that a simple stack of interleaved MLP layers — alternating between mixing along the time axis and mixing across feature channels — achieves strong forecasting accuracy while remaining computationally efficient and easy to interpret architecturally.