scholarly journals Stochastic Hydrologic Modeling in Real Time Using a Deterministic Model (Streamflow Synthesis and Reservoir Regulation Model), Time Series Model, and Kalman Filter

2000 ◽  
Author(s):  
Philip Tang
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Real Time ◽  
Hydrologic Modeling ◽  
Deterministic Model ◽  
Time Series Model ◽  
Reservoir Regulation ◽  
Model Time Series ◽  
Regulation Model

scholarly journals A Near Real-Time Method for Forest Change Detection Based on a Structural Time Series Model and the Kalman Filter

2020 ◽  
Vol 12 (19) ◽  
pp. 3135 ◽  
Author(s):  
Martin Puhm ◽  
Janik Deutscher ◽  
Manuela Hirschmugl ◽  
Andreas Wimmer ◽  
Ursula Schmitt ◽  
...  
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Change Detection ◽  
Real Time ◽  
Detection Method ◽  
Time Series Model ◽  
Forest Change ◽  
Vegetation Monitoring ◽  
Structural Time Series ◽  
Sentinel 2

The increasing availability of dense time series of earth observation data has incited a growing interest in time series analysis for vegetation monitoring and change detection. Vegetation monitoring algorithms need to deal with several time series characteristics such as seasonality, irregular sampling intervals, and signal artefacts. While common algorithms based on deterministic harmonic regression models account for intra-annual seasonality, inter-annual variations of the seasonal pattern related to shifts in vegetation phenology due to different temperature and rainfall are usually not accounted for. We propose a transition to stochastic modelling and present a near real-time change detection method that combines a structural time series model with the Kalman filter. The model continuously adapts to new observations and allows to better separate phenology-related deviations from vegetation anomalies or land cover changes. The method is tested in a forest change detection application aiming at the assessment of damages caused by storm events and insect calamities. Forest changes are detected based on the cumulative sum control chart (CUSUM) which is used to decide if new observations deviate from model-based forecasts. The performance is evaluated in two test sites, one in Malawi (dry tropical forest) and one in Austria (temperate deciduous, coniferous and mixed forests) based on Sentinel-2 time series. Both forest areas are characterized by a distinct, but temporally varying leaf-off season. The presented change detection method shows overall accuracies above 99%, users’ accuracies of 76.8% to 88.6%, and producers’ accuracies of 68.2% to 80.4% for the forest change stratum (minimum mapping unit: 0.1 ha). Results are based on visually interpreted points derived by stratified random sampling. A further analysis revealed that increasing the time series density by merging data from two Sentinel-2 orbits yields better forest change detection accuracies in comparison to using data from one orbit only. The resulting increase in users’ accuracy amounts to 7.6%. The presented method is capable of near real-time processing and could be used for a variety of automated forest monitoring applications.

scholarly journals Prediksi Pencurian Sepeda Motor Menggunakan Model Time Series (Studi Kasus: Polres Kotabumi Lampung Utara)

2020 ◽  
Vol 14 (3) ◽  
pp. 425-434
Author(s):  
MELI PRANATA ◽  
DIAN ANGGRAINI ◽  
Deden Makbuloh ◽  
Achi Rinaldi
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Model ◽  
Ar Model ◽  
Autoregressive Integrated Moving Average ◽  
Model Time Series

Tindak kriminal adalah kejahatan yang melanggar undang-undang suatu Negara atau melanggar norma yang berlaku dalam masyarakat. Pencurian merupakan salah satu bentuk dari perbuatan tindak kriminal. Dampak yang ditimbulkan dari adanya pencurian adalah perasaan kurang aman, takut, dan tenang. Salah satu model yang digunakan untuk memprediksi jumlah kasus pencurian yaitu model time series. Model time series adalah serangkaian nilai pengamatan yang diambil selama periode waktu tertentu. Pada umumnya, dalam interval-interval yang sama panjang, (Spuege & Stephens, 2004). Penelitian ini bertujuan memodelkan data tindak kriminal yang terjadi di Lampung Utara dengan model Autoregressive (AR), Moving Average (MA), dan Autoregressive Integrated Moving Average (ARIMA). Selanjutnya dari model terbaik akan digunakan untuk peramalan 6 bulan kedepan. Hasil penelitian model AR , model AR , model MA , ARIMA , dan model ARIMA . Model MA  memiliki koefisien parameter yang signifikan, memenuhi uji diagnostic tidak adanya residual pada model dan memiliki nilai RMSE dan AIC terkecil dengan nilai RMSE sebesar dan nilai AIC sebesar . Hasil prediksi model MA  untuk 6 bulan ke depan cenderung mendatar.

scholarly journals ANALISIS PERAMALAN PERMINTAAN PRODUK NATA DE COCO UNTUK MENDUKUNG PERENCANAAN DAN PENGENDALIAN PRODUKSI DALAM SUPPLY CHAIN DENGAN MODEL CPFR (COLLABORATIVE PLANNING, FORECASTING, AND REPLENISHMENT)

2017 ◽  
Vol 2 (2) ◽  
pp. 130
Author(s):  
Lina Saptaria
Keyword(s):  
Time Series ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Least Squares ◽  
Collaborative Planning ◽  
Time Series Model ◽  
Moving Averages ◽  
Chain Management ◽  
Model Time Series

Strategi proses bisnis yang dinilai efektif dalam penerapan SCM (Supply Chain Management) adalah penggunaan metode perencanaan kolaboratif, peramalan dan pengisian ulang atau CPFR (Collaborative Planning, Forecasting, And Replenishment) dimana produsen, distributor, dan pengecer bekerjasama untuk merencanakan, meramalkan, dan mengisi kembali produk yang dijual di pasar. Tujuan penelitian ini adalah untuk mendeskripsikan proses penyusunan peramalan kolaboratif antara mitra rantai pasok (Supply Chain) dengan mengacu pada alur kerja model CPFR dalam bisnis manufaktur skala mikro dan menemukan metode peramalan yang dinilai paling efektif dan efisien yang dapat digunakan sebagai dasar pembuatan keputusan dalam perencanaan dan pengendalian produksi sepanjang rantai pasok. Analisis peramalan menggunakan model time series dengan 3 metode yaitu Moving Averages dengan periode waktu 4 bulanan, Trend Projection metode exponential smooting dengan α = 0.1, α = 0.5, α = 0.9 dan Linier Regression (Least Squares). Hasil analisis  menunjukkan bahwa metode Linier Regression (Least Squares) merupakan metode yang paling efektif yang dapat digunakan sebagai dasar pembuatan keputusan dalam perencanaan dan pengendalian produksi sepanjang rantai pasok.   Kata Kunci: Peramalan Permintaan, Perencanaan dan Pengendalian Produksi, Metode Time Series, Model CPFR

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