TAILIEUCHUNG - Time Series Forecasting – Part I

Time Series Forecasting – Part I presents about What is a Time Series? Components of Time Series, Evaluation Methods of Forecast, Smoothing Methods of Time Series, Time series models, Components of a time series, Trend component. | Time Series Forecasting– Part I What is a Time Series ? Components of Time Series Evaluation Methods of Forecast Smoothing Methods of Time Series by Duong Tuan Anh Faculty of Computer Science and Engineering September 2011 What is a Time series ? A time series is a collection of observations made sequentially in time. 0 50 100 150 200 250 300 350 400 450 500 23 24 25 26 27 28 29 Examples: Financial time series, scientific time series Time series models Regression models Predict the response over time of the variable under study to changes in one or more of the explanatory variables. Deterministic models of time series Stochastic models of time series All the three kinds of models can be used for forecasting. Components of a time series The pattern or behavior of the data in a time series has several components. Theoretically, any time series can be decomposed into: Trend Cyclical Seasonal Irregular However, this decomposition is often not straight-forward because these factors interact. Trend component The trend component accounts for the gradual shifting of the time series to relatively higher or lower values over a long period of time. Trend is usually the result of long-term factors such as changes in the population, demographics, technology, or consumer preferences. Seasonal component The seasonal component accounts for regular patterns of variability within certain time periods, such as a year. The variability does not always correspond with the seasons of the year (. winter, spring, summer, fall). There can be, for example, within-week or within-day “seasonal” behavior. Cyclical component Any regular pattern of sequences of values above and below the trend line lasting more than one year can be attributed to the cyclical component. Usually, this component is due to multiyear cyclical movements in the economy. Evaluating Methods of forecasts Forecasting method is selected - many times by intuition, previous experience, or .

• Cơ sở dữ liệu
• Time Series Forecasting
• Components of Time Series
• Evaluation Methods of Forecast
• Smoothing Methods of Time Series
• Time series models
• Components of a time series
• Trend component
• Journal of Science and Technology
• The partitioning method
• Hedge algebras for fuzzy time series forecasting
• Fuzzy time series forecasting
• The accuracy of forecasting
• Forecasting time series
• Hedge algebras
• Particle swarm optimization
• Fuzzy time series
• Enrollment forecasting based
• Linguistic time series
• Forecasting model
• Linguistic logical relationship
• Information sciences
• New hybrid method
• Predicting univariate
• Multivariate time series
• Pattern forecasting
• Machine learning
• Hybrid model
• Linguistic logical relationship group
• Series forecasting model based
• Daily average temperature data
• Goodness of fit
• Modelling and structural time series model
• Structural time series model
• Chickpea production
• Forecasting of chickpea production
• Doctoral Dissertation
• Application of Markov chain models
• Markov chain models and fuzzy time series
• Time series modeling
• Fuzzy rules of time series
• Higher Markov chains
• Doctoral thesis summary
• Nitrogen content
• Forecasting of nitrogen content
• Soil by hybrid time series model
• Hybrid time series model
• Time series method
• Forecasting of biodiesel prices
• Biodiesel prices in Thailand
• Time series decomposition method
• Multiplicative model
• Time series
• Irrigated area under major crops
• ARIMA models
• Series modelling
• Forecasting of irrigated area
• Machine learning techniques
• Quantitative share price prediction
• S uitability of ARIMA model
• Time series analysis
• Mekong Delta
• Salinity intrusion
• Several models
• Adequate predictive model
• Information and communication technology
• Hybrid least squares support vector machine
• Cuckoo search algorithms
• Least squares support vector machine
• Autocorrelation function
• Autoregressive models AR
• Moving Average models MA
• ARMA models
• Estimating and checking ARIMA models
• Forecasting the impact
• Climate change
• Wheat production
• Wheat production in Varanasi district
• Ann models
• Production of pearl millet
• Crop of Karnataka
• ARIMA Models based
• Sheep and goatmeat
• Goat meat export
• Forecasting of sheep
• Time series modelling
• Kinh tế vĩ mô
• Dự báo tài chính
• Financial forecasting
• Macroeconomic forecasting
• Business cycle facts
• Time series data
• Information criteria clustering
• Extreme learning machine regression
• Wind forecasting
• Wind forecasting based
• Operations Management
• Lecture Operations management
• Supply chain management
• Forecasting in supply chain management
• Forecasting demand
• Time series methods
• ARIMA model
• Irrigated area
• Productivity of important cereal crops
• Forecasting of total area
• Day ahead electricity price forecasting
• Functional data analysis
• Functional principal components
• Functional time series forecasting
• Colombian electricity spot price
• Univariate time series modelling
• Stochastic processes
• Econometrics for Finance
• Introductory Econometrics for Finance
• Financial data
• Financial modelling
• Financial performance evaluation
• Analytical hierarchy process
• Topsis method
• Turkish banking sector
• Multi criteria decision making