পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| সরল ও দ্বৈত সূচকীয় মসৃণীকরণ (SES / Holt)× | ARIMA (Autoregressive Integrated Moving Average) মডেল× | স্ট্রাকচারাল টাইম সিরিজ মডেল (বেসিক স্ট্রাকচারাল মডেল)× | |
|---|---|---|---|
| ক্ষেত্র | অর্থমিতি | অর্থমিতি | অর্থমিতি |
| পরিবার | Regression model | Regression model | Regression model |
| উদ্ভবের বছর≠ | 1957 | 2015 | 1990 |
| প্রবর্তক≠ | Robert G. Brown (SES); Charles C. Holt (linear trend) | Box & Jenkins (Box-Jenkins methodology) | Andrew C. Harvey |
| ধরন≠ | Exponential smoothing forecasting model | Univariate time-series model | State-space (unobserved components) time series model |
| মৌলিক উৎস≠ | Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill. link ↗ | 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 | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 |
| অপর নাম≠ | SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) |
| সম্পর্কিত≠ | 3 | 5 | 4 |
| সারসংক্ষেপ≠ | Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta. | 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). | The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit. |
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