পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| ARIMA (Autoregressive Integrated Moving Average) মডেল× | দীর্ঘ-স্মৃতি মডেল (ARFIMA, FIGARCH)× | |
|---|---|---|
| ক্ষেত্র≠ | অর্থমিতি | অর্থায়ন |
| পরিবার | Regression model | Regression model |
| উদ্ভবের বছর≠ | 2015 | 1980 |
| প্রবর্তক≠ | Box & Jenkins (Box-Jenkins methodology) | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) |
| ধরন≠ | Univariate time-series model | Fractionally integrated time series model |
| মৌলিক উৎস≠ | 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 | Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29. DOI ↗ |
| অপর নাম≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | ARFIMA, FIGARCH, fractionally integrated models, fractional integration |
| সম্পর্কিত≠ | 5 | 4 |
| সারসংক্ষেপ≠ | 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). | Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration. |
| ScholarGateডেটাসেট ↗ |
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