Apa itu data time series

Metode yang sering digunakan adalah metode ARIMA Box-Jenkins yang digunakan untuk mengolah runtun waktu yang univariat dan metode analisis fungsi transfer digunakan untuk mengolah data runtun waktu multivariat. Untuk dapat diolah dengan menggunakan metode ARIMA Box-Jenkins, suatu data runtun waktu harus memenuhi syarat stasioneritas (Makridakis et.al, 1999).

Apa yang dimaksud dengan stasioneritas itu?

Stasioneritas berarti bahwa tidak terjadi pertumbuhan dan penurunan data. Untuk mendeteksi ketidakstasioneran data dalam mean dapat digunakan plot time series, plot fungsi autokorelasi (ACF) dan plot fungsi autokorelasi parsial (PACF). Jika data mengandung komponen trend, maka plot ACF/PACF akan meluruh secara perlahan dan data tidak stasioner dalam mean. Untuk mendeteksi ketidakstasioneran dalam varian dapat digunakan plot ACF/PACF dari residual kuadrat.

Lalu, bagaimana jika proses tidak stasioner?

Sebuah proses tidak stasioner dalam mean merupakan masalah yang sangat serius pada analisis data runtun waktu. Ketidakstasioneran dalam mean dapat distasionerkan dengan melakukan differensi derajat d. Untuk mendapatkan kestasioneran dapat dibuat deret baru yang terdiri dari differensi antara periode yang berurutan :

Selanjutnya kita cek plot ACF dan PACF-nya.

Autocorrelation Function (ACF)

Fungsi autokorelasi digunakan untuk menjelaskan suatu proses stokastik yang akan memberikan informasi bagaimana korelasi antara data (Zt) yang berdekatan.

Partial Autocorrelation Function (PACF)

Fungsi autokorelasi parsial yaitu himpunan autokorelasi parsial pada lag k.

Salah satu model yang dapat digunakan untuk peramalan (forecasting) data runtun waktu adalah model Autoregressive Integrated Moving Average (ARIMA). Model Autoregressive Integrated Moving Average (ARIMA) merupakan model ARMA nonstasioner yang telah didifferencing sehingga menjadi model stasioner. Bentuk umum model ARIMA adalah

Bagaimana cara menentukan model terbaik dan pengunaannya untuk peramalan?

Mari kita simak pembahasannya di video tutorial berikut:

Time series data is gathered, stored, visualized  and analyzed for various purposes across various domains:

1. In data mining, pattern recognition and machine learning, time series analysis is used for clustering, classification, query by content, anomaly detection and forecasting.
2. In signal processing, control engineering and communication engineering, time series data is used for signal detection and estimation.
3. In statistics, econometrics, quantitative finance, seismology, meteorology, and geophysics the time series analysis is used for forecasting.

Time series data can be visualized in different types of charts to facilitate insight extraction, trend analysis, and anomaly detection. Time series visualization and dashboarding tools include the InfluxDB UI and Grafana.

The term 'time series patterns' describes long-term changes in the series. Whether measured as a trend, seasonal, or cyclic pattern, the correlation can be calculated in a number of ways (linear, exponential, etc.), and the direction may change at any given time.

Time series data is used in time series analysis (historical or real-time) and time series forecasting to detect and predict patterns — essentially looking at change over time. Following is a brief overview of each.

Time series analysis methods

Time series analysis is a method of analyzing a series of data points collected over a period of time. In time series analysis, data points are recorded at regular intervals over a set period of time, rather than intermittently or at random.

Time series analysis is the use of statistical methods to analyze time series data and extract meaningful statistics and characteristics about the data. TSA helps identify trends, cycles, and seasonal variances to aid in the forecasting of a future event. Factors relevant to TSA include stationarity, seasonality and autocorrelation.

Time series analysis can be useful to see how a given variable changes over time (while time itself, in time series data, is often the independent variable). Time series analysis can also be used to examine how the changes associated with the chosen data point compare to shifts in other variables over the same time period.

Learn more about time series analysis methods, including spectral analysis, wavelet analysis, autocorrelation, and cross-correlation.

Time series forecasting methods

Time series forecasting uses information regarding historical values and associated patterns to predict future activity.

Time series forecasting methods include:

• Trend analysis
• Cyclical fluctuation analysis
• Seasonal pattern analysis

As with all forecasting methods, success is not guaranteed. Machine learning is often used for this purpose. So are its classical predecessors: Error, Trend, Seasonality Forecast (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt-Winters.

To ‘see things’ ahead of time, time series modeling (a forecasting method based on time series data) involves working on time-based data (years, days, hours, minutes) to derive hidden insights that inform decision-making. Time series models are very useful models when you have serially correlated data. Most businesses work on time series data to analyze sales projections for the next year, website traffic, competitive positioning and much more.

Learn more about time series forecasting methods, including decompositional models, smoothing-based models, and models including seasonality.

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