Introduction to Time Series Analysis and Forecasting in R

Learn to handle date and time data across formats, convert character columns to date time, and use Posixct, Posixlt, strptime, and Lubridate.

Explore base date-time classes in R, compare POSIXt, Date, and Chron, and use cron and lubridate to handle time zones, formats, and leap years since 1970.

Convert character date-time data to POSIXt in R with strptime. Learn to specify the format argument using codes like %Y, %m, %d, %H, and %M for uniform dataframe columns.

Explore the Lubridate package for robust date and time manipulation in R, covering parsing with ymd/mdy/dmy, time zone handling, intervals, and weekday extraction.

Construct a seven-row data frame with date, time, and measurement columns using date and time input formats. Use lubridate to convert to date and time and combine vectors with cbind.

Use lubridate to perform time calculations with the minute and d minutes functions, noting integer versus decimal inputs. Distinguish period and duration to convert to seconds and understand leap years.

Practice a lubridate data exercise. Create a 2014 time point in central european time, adjust minutes to seven, and compare with a 2015 point to compute a 608.48 day difference.

Create time series in R by converting vectors with the RTS class, attaching time stamps, and specifying start and frequency to handle regular and univariate or multivariate data.

Create a time series object in R by generating random numbers, taking their cumulative sum, and plotting with base or lattice plots, starting November 1914 and frequency 12.

Learn to create and customize a season plot with the forecast package in R, using the air passengers dataset and options for year labels, label gaps, and plot type.

Import data from Excel or other sources into an R time series, using a monthly German inflation dataset (2008–2017) to create a ts object with start and frequency 12.

Learn to handle irregular time series in R by regularizing data through daily aggregation. Use zoo, posixct/posixlt, and strptime to convert dates and build a reliable regular time series.

Learn to detect and handle missing data and outliers in time series with R. Use forecast and zoo to impute values, identify outliers, and clean data with ts clean.

Analyze time series vectors in R by attaching timestamps, understanding order and lag concepts, and analyzing univariate statistics such as mean, median, and quantiles.

Examine time series traits like trend, seasonality, and changing variance, learn to assess stationarity, and prepare data for arima modeling.

Explore simple forecasting methods such as mean, naive last observation carried forward, and drift, showing they can rival Arima and exponential smoothing, especially on random stock or financial data.

Compare time series models using accuracy metrics such as mae, rmse, mase, mape, and aic, with training/test splits and simple models to identify the best forecast.

Learn how residuals reveal the randomness left after fitting a time series model, signaling zero mean, constant variance, and uncorrelated behavior, with checks via histograms and acf toward normality.

Explore stationarity in time series, apply transformations and differencing to stabilize data, and use ADF tests in R to guide ARIMA or exponential smoothing modeling.

Explore autocorrelation in time series data, using lags, the autocorrelation function (ACF) and partial autocorrelation (Pacf), and apply the Durbin-watson test in R.

Explore how the acf and pacf functions in R reveal autocorrelation and partial autocorrelation to identify the MA and AR parts in ARIMA models, with practical plots and interpretation.

Explore a self generated time series by comparing mean, naive, and drift forecasts, assess accuracy, diagnose residuals, and apply a log transformation to improve variance, with naive performing best.

Explore the spectrum of quantitative time series models, from linear approaches like ARIMA and exponential smoothing to non-linear neural nets, with emphasis on model comparisons, seasonality, and multivariate extensions.

Study seasonal decomposition for univariate time series with a seasonal component, using additive or multiplicative models, and compare decompose with STL for quick trend and seasonality insights.

Learn to decompose time series with the decompose function to extract trend, seasonality, and remainder, create seasonally adjusted data, and forecast with Arima and STL methods in R.

Plot the Air Passengers time series, identify trend and seasonal patterns, then decompose with additive and multiplicative models using decompose; compare results and obtain a seasonally adjusted series.

Learn how simple moving average smooths time series to reveal trends by averaging consecutive values, using R and packages TR or TTR. Understand how period length affects smoothing and delay.

Explore exponential smoothing with ets in R, covering additive and multiplicative error, trend, and seasonality, with Holt-Winters variants and automated model selection for forecasts.

Explore qualitative forecasting using judgmental methods when data is unavailable, including the Delphi method, forecasting by analogy, and scenario analysis to anticipate uncertain, high-impact events.

Explore the Arima model system for univariate time series, learn its three parameters, and compare theory, Auto Arima implementation, simulation, manual selection, and forecasting in R.

Explore univariate non-seasonal arima models and the box-jenkins framework, including p, d, q parameters, differencing, and stationarity. See how auto.arima from the forecast package automates parameter selection and model fitting.

Explore time series analysis with auto.arima in the forecast package, using the Lynx data, ACF and PCF plots, stationarity checks, and information criteria for robust benchmarks.

Demystify ARIMA model calculations in R by examining AR and MA terms, constants and mean adjustments, in-sample fits, residuals, and Kalman filter concepts using the Lynx dataset.

Simulate time series with arima models in R using arima sim, and evaluate stationarity and model parameters with adf, acf, pacf, and auto.arima.

Master manual arima parameter selection in r by comparing base and forecast arima, testing stationarity with ADF, selecting p and q via ACF and PACF, and validating residuals, alongside auto.arima.

Identify ARIMA parameters by applying differencing rules (D), P, Q, and seasonal components using ACF and PACF plots; compare models with information criteria.

Forecast with an ARIMA model using the forecast package, generate a ten-year Lynx forecast with 80% and 95% intervals, and compare to an ETS model using ggplot2 autoplot.

Learn to model multivariate time series in R using vector autoregressive models, diagnose stationarity, apply Granger causality, and forecast with data from EU stock indices.

Explore the three data structures of time series—univariate, univariate with external regressors, and classic multivariate—and learn how these shapes guide model choices like arima, neural networks, and var.

Convert a data frame into a multivariate time series using EMTs objects in R, set the frequency and start, and explore the stock market dataset (DAX, SMI, CAC, FTSE).

Explore key R packages for multivariate time series analysis, including Mtz and VARs, and learn to manage var function differences and package conflicts in vector autoregressive modeling.

Test each column for stationarity with the ADF test in the TI series package, assess p-values against 0.05, then apply DFM to difference and retest for VAR readiness.

Explore the vector autoregressive model theory, showing how multivariate time series rely on endogenous relationships, lag structures, and Varma models, with lag order selection using AIC and Granger causality checks.

Learn how to implement a var model in R, identify the optimal lag order with var select and var, and forecast using a none type, stationary multivariate time series.

Assess multivariate series models by testing residual correlation with a portmanteau test via serial.test on a var object to optimize model fit; consider differencing or logarithms when residuals remain correlated.

Identify the right variables and lag order for a var model. Apply the Granger causality test in R to assess whether one variable influences others, ensuring stationarity.

Forecasting a var model requires reversing differencing and producing a 25-day ahead forecast for the Dax index, with multivariate plots in base, ggplot2, and lattice.

Explore how neural networks apply to time series analysis in R, including R packages, univariate and multivariate forecasting with the network function, and demonstrations using smart meter data.

Explore neural networks for time series analysis, focusing on autoregression based models, lag inputs, seasonal lags, and the multilayer feedforward structure, plus data de-trending and result aggregation.

Explore the apt electricity dataset from a smart meter to model high-frequency watt measurements with daily seasonality in time series forecasting in R, using appliances as an external regressor.

Explore neural nets for time series in R via the time series task view, comparing tsd, gmd, and n for packages, with forecast's neural net function as the easiest option.

Explore neural networks in R using the forecast package's enter function for univariate time series with configurable P, size, repeats, and xreg.

Fit a univariate autoregressive neural network to watt data with a 288 seasonal cycle, using last 14 lags plus one seasonal lag, then forecast 400 steps.

Apply neural nets to multivariate time series with external regressors, ensuring numeric inputs and unidirectional influence. Use appliances as regressor to forecast ten hours and compare with a univariate model.

Explore resources for time series analysis, including Hindman’s forecasting principles and practice and the Penn State course. R remains the primary tool, with Python interop via Statsmodels and StackOverflow guidance.