ứng dụng mô hình arima để dự báo vnindex

... Tuyển tập Báo cáo Hội nghị Sinh viên Nghiên cứu Khoa học lần thứ 7 Đại học Đà Nẵng năm 2010 82 ỨNG DỤNG MÔ HÌNH ARIMA ĐỂ DỰ BÁO VNINDEX APPLICATION OF ARIMA MODEL TO FORECAST VNINDEX SVTH: ... sách sẽ làm cho sai số dự báo tăng cao hơn. Do đó kết quả của mô hình vẫn chỉ mang tính chất tham khảo nhiều hơn. Tuy nhiên có thể nói mô hình ARIMA là một mô hình tốt để dự báo trong ngắn hạn. ... xuất sử dụng mô hình ARIMA và phương pháp Box-jenkins để dự báo chỉ số VnIndex trong ngắn hạn căn cứ vào chuỗi dữ liệu quá khứ. George Box và Gwilym Jenkins (1976) đã nghiên cứu mô hình ARIMA...

... forecast BÁO CÁO MÔN KINH TẾ LƯỢNG ỨNG DỤNG MÔ HÌNH ARIMA TRONG DỰ BÁO GIÁ DẦU THÔ THẾ GIỚI Sinh viên thực hiện: Đặng Thị Thu Hiền Mã sinh viên: CQ500927 Giới thiệu về mô hình Arima Trong ... nó trong công tác dự báo. Để trả lời câu hỏi này có nhiều phương pháp khác nhau trong đó có phương pháp sử dụng mô hình Arima để nghiên cứu cho kết quả khá tốt. Arima là mô hình trung bình trượt ... chúng tôi đề xuất sử dụng mô hình ARIMA và phương pháp Box-jenkins để dự báo giá dầu thô thế giới trong ngắn hạn căn cứ vào chuỗi dữ liệu quá khứ. II. Xây dựng mô hình Arima cho giá dầu thô...

... toán dùng cho dự báo lũ hoặc đầu vào cho mô hình thuỷ lực. Mô hình HMS là mô hình có ít tham số và dể sử dụng, không yêu cầu cao về tài liệu địa hình lưu vực, độ chính xác của mô hình cũng đã ... Việc dự báo lưu lượng lũ xảy ra ở thượng nguồn để làm cơ sở tính toán cho các mô hình thủy lực là rất cần thiết. Trong bài viết nầy chúng tôi đã nghiên cứu ứng dụng phần mền Hec-Hms để mô hình ... chọn mô hình này để áp dụng tính toán dòng chảy tại các biên của mô hình thuỷ lực. 2. Nội dung 2.1. Cơ sở lí thuyết của mô hình Hec-hms Trình tự tính toán theo các bước: - Các mô hình...

... dự báo bằng các mô hình số trị, bao gồm cả mô hình khí hậu toàn cầu và mô hình khí hậu khu vực. Trước khi các mô hình số trị được ứng dụng rộng rãi, phương pháp thống kê đã được sử dụng để ... nghiệm bước đầu ứng dụng mô hình khí hậu khu vực RegCM3 để dự báo nhiệt độ trung bình tháng và tổng lượng mưa tháng cho ba tháng mùa hè 6−8/1996 khi sử dụng sản phẩm kết xuất của mô hình khí hậu ... luận Nhằm hướng tới ứng dụng bộ mô hình khí hậu toàn cầu và mô hình khí hậu khu vực cho mục đích dự báo hạn mùa các trường khí hậu bề mặt ở Việt Nam, một số thử nghiệm về dự báo nhiệt độ trung...

... Box-Jenkins để xây dựng mô hình ARIMA cho dự báo lượng khách quốc tế đến Việt Nam dựa trên số liệu công bố hàng tháng của Tổng cục Du lịch Việt Nam. Kết quả cho thấy trong số các mô hình ước lượng ... U t Bước 4: Dự báo Những dự báo ngắn hạn về lượng khách du lịch quốc tế đến Việt Nam dựa trên mô hình ARIMA (12, 1,12) được trình bày trong Bảng 5. Bảng 5 cho thấy số liệu dự báo lượng khách ... DTOURISTARRIVAL là chuỗi dừng. 3.2. Xây dựng mô hình ARIMA cho chuỗi biến động lượng khách quốc tế đến Việt N am Để xây dựng mô hình ARIMA chúng tôi sử dụng chuỗi dữ liệu gồm 192 quan sát từ...

... W t-1 10 MÔ HÌNH ARIMA MÔ HÌNH ARIMA Theo Box- Jenkin mọi quá trình ngẫu nhiên có tính dừng đều có thể biểu diễn bằng mô hình ARIMA 4  Đối với các chuỗi thời gian  ARIMA thường được sử dụng để dự báo  ... Bình Trượt  Ứng dụng dự báo giá cá sông tại Tp. HCM 5 MÔ HÌNH ARIMA MÔ HÌNH ARIMA  Tính dừng (Stationary)  Tính mùa vụ (Seasonality)  Nguyên lý Box-Jenkin  Nhận dạng mô hình ARIMA  Xác ... 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 GIỚI THIỆU GIỚI THIỆU  Mô hình nhân quả  Mô hình chuỗi thời gian Hai loại mô hình dự báo chính: 2 NỘI DUNGNỘI DUNG  Giới thiệu xây dựng Mô Hình ARIMA (Auto-Regressive Integrated...

... pháp để mở rộng ứng dụng của mô hình DEA trong đánh giá năng suất và hiệu quả sản xuất. Ngoài ra, một nghiên cứu thực nghiệm ứng dụng các công cụ trên được thực hiện đối với hai mô hình sản ... áp dụng mô hình luân canh lúa-đậu phộng-lúa. Tạp chí Khoa học 2011:18a 210-219 Trường Đại học Cần Thơ 210 SỬ DỤNG CÔNG CỤ METAFRONTIER VÀ METATECHNOLOGY RATIO ĐỂ MỞ RỘNG ỨNG DỤNG MÔ HÌNH ... theo mô hình độc canh ba vụ lúa và 46 hộ ứng dụng mô hình sản xuất luân canh hai vụ lúa một vụ đậu phộng tại huyện Cầu Kè, tỉnh Trà Vinh. Kết quả ước lượng chỉ ra rằng hộ sản xuất theo mô hình...

... Nẵng. Tổng hợp, phân tích để đưa ra kết luận. 2. 2. Nội dung 2.1. Mô hình HOLSAT (HOLiday SATisfaction) Tribe và Snaith (1998) phát triển mô hình HOLSAT và sử dụng nó để đánh giá sự hài lòng ... lô. Dựa vào các kết quả phân tích để đưa ra những nhận xét và ý kiến đóng góp cho du lịch Đà Nẵng. 1.4. Phương pháp nghiên cứu. Dựa vào các nghiên cứu về mô hình HOLSAT của các tác giả để ... rằng sự hài lòng của du khách là một yếu tố quan trọng trong hoạt động du lịch. Việc sử dụng mô hình HOLSAT để đo lường sự hài lòng của khách du lịch quốc tế tại Đà Nẵng mang lại một cách thức...

... tài ỨNG DỤNG MÔ HÌNH IDIC ĐỂ THIẾT KẾ VÀ CÀI ĐẶT HỆ THỐNG THÔNG TIN HỖ TRỢ VÀ CHĂM SÓC KHÁCH HÀNG (CRM)” 2. Mục tiêu của đề tài - Nghiên cứu tổng quan về mô hình IDIC - Ứng dụng mô hình ... Bài báo cáo này giới thiệu hệ thống do chúng tôi phát triển với mục đích ứng dụng mô hình IDIC để thiết kế và cài đặt hệ thống thông tin hỗ trợ và chăm sóc khách hàng (CRM). Hệ thống sử dụng ... Tuyển tập Báo cáo Hội nghị Sinh viên Nghiên cứu Khoa học lần thứ 8 Đại học Đà Nẵng năm 2012 1 ỨNG DỤNG MÔ HÌNH IDIC ĐỂ THIẾT KẾ VÀ CÀI ĐẶT HỆ THỐNG THÔNG TIN...

... -  Tuyển tập Báo cáo Hội nghị Sinh viên Nghiên cứu Khoa học lần thứ 8 Đại học Đà Nẵng năm 2012 1 ỨNG DỤNG MÔ HÌNH MIKE ĐỂ TÍNH TOÁN NGẬP LỤT HẠ LƯU LƯU VỰC SÔNG ... các trận lũ năm 2008 và năm 2009 để hiệu chỉnh và kiểm tra mô hình; đã tìm được bộ tham số mô hình nhằm làm cơ sở cho tính toán dự báo cho các kịch bản phát triển độ thị hạ lưu sông Cu Đê. ... giả áp dụng MIKE FLOOD, một modun kết nối MIKE11 và MIKE21 thuộc bộ phần mềm MIKE của Viện khoa thủy lực Đan Mạch, để tính toán. Tác giả đã dựa vào các trận lũ năm 2008 và năm 2009 để hiệu...

... liệu đầu vào trong SWAT. Trên mỗi đơn vị diện tích ứng với mỗi loại hình sử dụng đất, SWAT sẽ ứng dụng các mô hình để tính toán, xác định, mô phỏng sự di chuyển chất ô nhiễm trong đất, nước ... Hình 2: Quy trình ứng dụng mô hình SWAT trong nghiên cứu xói mòn đất ở xã Dương Hòa HỘI THẢO ỨNG DỤNG GIS TOÀN QUỐC 2011 313 3. TỔNG QUAN CÁC VẤN ĐỀ NGHIÊN CỨU 3.1. Giới thiệu về mô hình ... Kim Lợi (2009). Ứng dụng mã nguồn mở trong đánh giá tác động của môi trường. Trường Đại học Nông lâm thành phố Hồ Chí Minh. 9. Nguyễn Kim Lợi (2009). Ứng dụng mô hình SWAT để đánh giá chất...

... đây em sẽ đưa ra một phương pháp khác - ứng dụng mô hình LOGISTIC để xếp hạng khách hàng doanh nghiệp tại ngân hàng VPBank – Ngô Quyền. 3.1. Ứng dụng mô hình LOGIT với đầy đủ các biến số vào ... X10 Estimation Equation: ỨNG DỤNG MÔ HÌNH LOGISTIC ĐỂ XẾP HẠNG KHÁCH HÀNG DOANH NGHIỆP TẠI NGÂN HÀNG VPBANK - NGÔ QUYỀN Có rất nhiều phương pháp để xếp hạng tín dụng đối với khách hàng doanh ... đồng tín dụng 3.2.5. Sử dụng các nghiệp vụ phái sinh tín dụng để phòng ngừa rủi ro Ngày nay các nhà quản lý rủi ro đang được tập trung vào hai lĩnh vực. Thứ nhất, phát triển các mô hình để đo...

... ca mô hình. c lng các h s ca mô hình: -Mô hình MA(q) áp dng phng pháp lp di dng quét. Bc 3: -H s ca mô hình phi khác 0, nu không tha mãn thì loi ra khi mô hình ... d báo. + 2 mô hình ARMA m rng: - Mô hình ARIMA: Áp dng cho chui có xu th không n đnh tng hay gim theo thi gian, ta đa chui v dng dng loi b yu t xu th. - Mô hình SARIMA: ... các h mô hình ARMA và đa ra mô hình nào thích hp và ti u nht. -Xác đnh p, q ca mô hình nh vào biu đ tng quan (Phân tích đ th trong các trng hp đn gin) Bc 2: -Mô hình...