các mô hình chuỗi thời gian

... Ước lượng các tham số của mô hình LSTR 22 1.2.3. Kiểm ñịnh thu hẹp mô hình 22 1.2.4. ðánh giá chất lượng mô hình bằng các kiểm ñịnh 23 1.3. Tổng quan về nghiên cứu mô hình chuỗi thời gian chuyển ... [60], áp dụng mô hình tự hồi quy chuyển tiếp trơn (STAR) mô hình hoá mô hình vĩ mô của nền kinh tế Nam Phi. Kết quả cho thấy các mô hình phi tuyến cho kết quả dự báo tốt hơn mô hình tuyến tính. ... của lớp mô hình chuỗi thời gian phi tuyến, cụ thể là nghiên cứu mô hình hồi quy chuyển tiếp trơn STR và một số trường hợp riêng của họ mô hình hồi quy chuyển tiếp trơn này. - Vì lớp mô hình chuyển...

... hình một số thuật toán dự báo trong mô hình chuỗi thời gian mờ 41 2.1. Mô hình thuật toán của Song và Chissom 41 2.2. Mô hình thuật toán của Chen 42 2.3. Thuật toán của Singh 43 2.4. Mô hình ... trong mô hình Box-Jenkins để loại bỏ tính không dừng của chuỗi thời gian. Huarng đã sử dụng các thông tin có trước trong tính chất của chuỗi thời gian như mức độ tăng giảm để đưa ra mô hình ... heuristic chuỗi thời gian mờ. Trong thời gian gần đây, đề tài này vẫn luôn được một số tác giả nghiên cứu. Các hướng hiện nay vẫn là tập trung nâng cao độ chính xác dự báo của mô hình chuỗi thời gian...

... hình một số thuật toán dự báo trong mô hình chuỗi thời gian mờ 41 2.1. Mô hình thuật toán của Song và Chissom 41 2.2. Mô hình thuật toán của Chen 42 2.3. Thuật toán của Singh 43 2.4. Mô hình ... 2. Mô hình một số thuật toán dự báo trong mô hình chuỗi thời gian mờ 2.1. Mô hình thuật toán của Song và Chissom Trong phần này, sử dụng khái niệm và phương pháp dự báo của chuỗi thời gian ... 5 CHƢƠNG 1 CÁC KIẾN THỨC CƠ BẢN VỀ CHUỖI THỜI GIAN Trong phần này, chúng ta sẽ tìm hiểu về một lớp mô hình chuỗi thời gian hết sức thông dụng trong thực tế. Đó là mô hình quy trình trượt...

... 2. Mô hình một số thuật toán dự báo trong mô hình chuỗi thời gian mờ 2.1. Mô hình thuật toán của Song và Chissom Trong phần này, sử dụng khái niệm và phương pháp dự báo của chuỗi thời gian ... hình một số thuật toán dự báo trong mô hình chuỗi thời gian mờ 41 2.1. Mô hình thuật toán của Song và Chissom 41 2.2. Mô hình thuật toán của Chen 42 2.3. Thuật toán của Singh 43 2.4. Mô hình ... .)(2qmnSHR 4. Những hạn chế của mô hình ARMA trong chuỗi thời gian tài chính Mô hình ARMA thu được thành công lớn khi áp dụng cho các chuỗi thời gian xuất phát từ các lĩnh vực khoa học tự nhiên...

... hiểu về việc sử dụng mô hình chuỗi thời gian trong dự báo, đặc biệt là việc sử dụng mô hình chuỗi thời gian mờ bậc cao, em đã lựa chọn đề tài “Ứng dụng mô hình chuỗi thời gian mờ bậc cao trong ... http://www.lrc-tnu.edu.vn CHƢƠNG 1 CÁC KIẾN THỨC CƠ BẢN VỀ CHUỖI THỜI GIAN Chƣơng 1 giới thiệu các kiến thức cơ bản về chuỗi thời gian và trọng tâm là trình bầy về một lớp mô hình chuỗi thời gian hết sức thông ... khi áp dụng với chuỗi thời gian tài chính. 1.1. Chuỗi thời gian và quá trình ngẫu nhiên 1.1.1. Khái niệm chuỗi thời gian và quá trình ngẫu nhiên Một chuỗi thời gian là một dãy các giá trị quan...

... của một chuỗi thời gian chỉ ra hình dạng của chuỗi thời gian phụ thuộc vào trạng thái của nó, do đó các trạng thái hiện hành xác định các mơ hình chuỗi thời gian. Nếu một chuỗi thời gian là ... Chuỗi thời gian đa biến 21 2.1.3.5 Chuỗi thời gian hỗn loạn 22 2.1.4 Mơ hình chuỗi thời gian 22 2.2 Chuỗi thời gian mờ 23 2.2.1 Khái niệm 23 2.2.2 Một số định nghĩa liên quan đến chuỗi thời ... CHƢƠNG II 17 MƠ HÌNH CHUỖI THỜI GIAN MỜ VÀ CÁC THUẬT TỐN CƠ BẢN 17 2.1 Các kiến thức cơ bản về chuỗi thời gian 17 2.1.1 Khái niệm chuỗi thời gian 17 2.1.2 Tính chất của chuỗi thời gian 17 2.1.2.1...

... Mô hình toán học dùng cho phân tích chuỗi thời gian thông thƣờng gồm:  Mô hình hồi quy.  Mô hình miền thời gian.  Mô hình miền tần số. Trong đó mô hình miền thời gian bao gồm:  Mô hình ... Phân chia chuỗi thời gian 8 1.2 Mô hình chuỗi thời gian 10 1.3 Mô hình hồi quy 10 1.3.1 Mô hình tự hồi quy (AR) 11 1.3.2 Mô hình trung bình trượt (MA) 12 1.1.4 Những hạn chế của mô hình ARMA ... về việc sử dụng mô hình chuỗi thời gian mờ trong dự báo, đặc biệt là việc sử dụng mô hình chuỗi thời gian mờ có trọng số bậc cao, em đã lựa chọn đề tài Mô hình chuỗi thời gian mờ có trọng...

... loại chuỗi thời gian 24 2.1.3.2 Chuỗi thời gian tuyến tính 25 2.1.3.2 Chuỗi thời gian phi tuyến 25 2.1.3.3 Chuỗi thời gian đơn biến 25 2.1.3.4 Chuỗi thời gian đa biến 25 2.1.3.5 Chuỗi thời gian ... của một chuỗi thời gian chỉ ra hình dạng của chuỗi thời gian phụ thuộc vào trạng thái của nó, do đó các trạng thái hiện hành xác định các mô hình chuỗi thời gian. Nếu một chuỗi thời gian là ... 2.1.4 Mô hình chuỗi thời gian 26 2.2. Chuỗi thời gian mờ 27 2.2.1. Khái niệm 27 2.2.2. Một số định nghĩa liên quan đến chuỗi thời gian mờ 27 2.3. Một số thuật toán trong mô hình chuỗi thời gian...

... của Ui.2.2 Chuỗi thời gian, làm trơn và ngoại suy chuỗi thời gian Số liệu nguồn vốn huy động và các số liệu khác là số liệu được thu thập theo thời gian hay còn gọi là chuỗi thời gian. Vì vậy, ... rất khó tìm ra quy luật chi phối các mối quan hệ. Vì thế để nghiên cứu các vấn đề kinh tế chúng ta phải sử dụng phương pháp mô hình. Mô hình toán kinh tế là mô hình kinh tế được trình bày bằng ... chúng ta cần mô hình hóa chuỗi này. Phân tích chuỗi thời gian là một phương pháp ngoại suy phức tạp. Nguyễn Thị TrangLớp: Toán Kinh tế 4824Chuyên đề thực tập tốt nghiệp Mô hình ngoại suy...

... 0,018.T2 - 27,20.V2 - 0,067.tĐ2. Mô hình này được sử dụng để dự đoán thời gian sấy thóc trong thiết bị sấy vỉ ngang. Từ khóa: mô hình, thóc, thời gian sấy, lớp dầy. SUMMARY This study ...  ?aC&'*$&K)LF$C Hình 5. Ảnh hưởng của chiều dầy lớp hạt Mô hình hóa thời gian sấy riêng trong sấy thóc tĩnh theo lớp dầy 821 N)/#3}T‚=JAe||48‚I>u3c8V#' ... NGHIỆP HÀ NỘI 814 MÔ HÌNH HÓA THỜI GIAN SẤY RIÊNG TRONG SẤY THÓC TĨNH THEO LỚP DẦY Modelling of Specific Drying Time in Stationary Deep-bed Layer Drying of Paddy Đỗ Thái Sơn Bộ môn Kỹ thuật Nhiệt,...

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 3GIỚI THIỆU GIỚI THIỆU  Mô hình nhân quả Mô hình chuỗi thời gian Hai loại mô hình dự báo chính: 2NỘI DUNGNỘI DUNG Giới thiệu xây dựng Mô Hình ARIMA(Auto-Regressive Integrated ... 5MÔ HÌNH ARIMA MÔ HÌNH ARIMA  Tính dừng (Stationary) Tính mùa vụ (Seasonality) Nguyên lý Box-Jenkin Nhận dạng mô hình ARIMA Xác định thông số mô hình ARIMA Kiểm định về mô hình ... diễnbằng mô hình ARIMA 4 Đối với các chuỗi thời gian  ARIMA thường được sử dụng để dự báo Theo mô hình ARIMA, giá trị dự báo sẽ phụ thuộc vào các giá trị quá khứ và tổng có trọng số các nhiễu...